The Denver Nuggets, a prominent team in the NBA, have consistently showcased a roster filled with talent and versatility. This analysis delves into the performance metrics and contributions of key players, providing insights into their strengths and areas for improvement. By examining statistical data, we can better understand how each player impacts the team's overall success and identify trends that could influence future strategies.
Nikola Jokic: As the cornerstone of the Nuggets, Jokic's exceptional skills in scoring, rebounding, and playmaking have earned him multiple MVP awards. His ability to consistently deliver triple-doubles makes him a pivotal player in the team's lineup.
Jamal Murray: Known for his scoring prowess and clutch performances, Murray's dynamic playstyle complements Jokic's versatility. His ability to perform under pressure is crucial during critical moments in games.
Michael Porter Jr.: Porter's scoring ability and three-point shooting provide the Nuggets with a valuable offensive weapon. His contributions are vital in stretching the floor and creating scoring opportunities.
Aaron Gordon: Gordon's athleticism and defensive capabilities add a significant dimension to the Nuggets' gameplay. His versatility allows him to guard multiple positions and contribute on both ends of the court.
Russell Westbrook: Westbrook has an EFF score of 600. Known for his explosive athleticism and triple-double capabilities, he brings energy and intensity to the court.
Christian Braun: As a promising young talent, Braun's all-around game and defensive tenacity make him a valuable asset. His development will be key to the Nuggets' future success.
This analysis will utilize various statistical metrics, including points per game, shooting percentages, rebounds, assists, and efficiency ratings, to evaluate player performance. By leveraging data visualization tools, we can identify patterns and trends that highlight each player's impact on the team's performance.
import pandas as pd
import numpy as np
# Display all columns
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
from matplotlib.patches import FancyBboxPatch
import matplotlib.transforms as transforms
import matplotlib.gridspec as gridspec
import matplotlib.ticker as mtick
import matplotlib.pyplot as plt
from matplotlib import style, offsetbox
import seaborn as sns
import matplotlib
style.use('ggplot')
%matplotlib inline
#sns.set_palette("dark")
#style.use('ggplot')
#sns.set_style('darkgrid')
matplotlib.rcParams['font.size'] = 15
matplotlib.rcParams['font.weight'] = 'bold'
#matplotlib.rcParams['figure.figsize'] = (50, 20)
#matplotlib.rcParams['figure.facecolor'] = '#00000000'
nugget_regular_season_df = pd.read_csv('2024-2025 Denver Nuggets Players Stats.csv')
nugget_regular_season_df
| Date | Hm/Aw | Opp | W/L | MIN | Pts | PLAYER | FGM | FGA | FG% | 3PM | 3PA | 2PM | 2PA | FTM | FTA | OREB | DREB | REB | AST | STL | BLK | TOV | PF | +/- | EFF | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2024-10-25 | vs | OKC | L | 35 | 16 | Nikola Jokic | 6 | 13 | 46.2% | 1 | 3 | 5 | 10 | 3 | 4 | 4 | 8 | 12 | 13 | 2 | 1 | 3 | 3 | -9 | 33 |
| 1 | 2024-10-25 | vs | OKC | L | 29 | 16 | Christian Braun | 8 | 15 | 53.3% | 0 | 3 | 8 | 12 | 0 | 0 | 2 | 5 | 7 | 1 | 2 | 2 | 3 | 1 | 8 | 18 |
| 2 | 2024-10-25 | vs | OKC | L | 32 | 15 | Michael Porter | 5 | 17 | 29.4% | 3 | 10 | 2 | 7 | 2 | 2 | 1 | 7 | 8 | 2 | 1 | 0 | 1 | 2 | -2 | 13 |
| 3 | 2024-10-25 | vs | OKC | L | 33 | 12 | Aaron Gordon | 5 | 12 | 41.7% | 0 | 1 | 5 | 11 | 2 | 2 | 5 | 4 | 9 | 2 | 1 | 0 | 2 | 1 | -4 | 15 |
| 4 | 2024-10-25 | vs | OKC | L | 38 | 12 | Jamal Murray | 4 | 13 | 30.8% | 2 | 5 | 2 | 8 | 2 | 2 | 2 | 4 | 6 | 4 | 2 | 0 | 3 | 2 | -2 | 12 |
| 5 | 2024-10-25 | vs | OKC | L | 21 | 6 | Russell Westbrook | 2 | 10 | 20.0% | 1 | 6 | 1 | 4 | 1 | 4 | 1 | 4 | 5 | 5 | 1 | 2 | 2 | 0 | -24 | 6 |
| 6 | 2024-10-25 | vs | OKC | L | 17 | 6 | Julian Strawther | 3 | 6 | 50.0% | 0 | 2 | 3 | 4 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 2 | -23 | 4 |
| 7 | 2024-10-25 | vs | OKC | L | 15 | 2 | Peyton Watson | 1 | 7 | 14.3% | 0 | 4 | 1 | 3 | 0 | 0 | 2 | 4 | 6 | 1 | 0 | 2 | 0 | 0 | -13 | 5 |
| 8 | 2024-10-25 | vs | OKC | L | 11 | 2 | Dario Saric | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | -6 | 3 |
| 9 | 2024-10-25 | vs | OKC | L | 2 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 10 | 2024-10-25 | vs | OKC | L | 2 | 0 | Vlatko Cancar | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 11 | 2024-10-25 | vs | OKC | L | 2 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 2024-10-25 | vs | OKC | L | 2 | 0 | Jalen Pickett | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 13 | 2024-10-25 | vs | OKC | L | 2 | 0 | Zeke Nnaji | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 14 | 2024-10-26 | vs | LAC | L | 37 | 41 | Nikola Jokic | 14 | 26 | 53.8% | 7 | 12 | 7 | 14 | 6 | 8 | 3 | 6 | 9 | 4 | 2 | 1 | 5 | 2 | 7 | 38 |
| 15 | 2024-10-26 | vs | LAC | L | 37 | 22 | Jamal Murray | 7 | 14 | 50.0% | 3 | 6 | 4 | 8 | 5 | 5 | 0 | 2 | 2 | 5 | 2 | 0 | 2 | 1 | 6 | 22 |
| 16 | 2024-10-26 | vs | LAC | L | 34 | 11 | Christian Braun | 4 | 6 | 66.7% | 1 | 1 | 3 | 5 | 2 | 2 | 1 | 6 | 7 | 1 | 1 | 2 | 1 | 3 | 1 | 19 |
| 17 | 2024-10-26 | vs | LAC | L | 36 | 10 | Aaron Gordon | 3 | 10 | 30.0% | 1 | 4 | 2 | 6 | 3 | 4 | 2 | 3 | 5 | 2 | 1 | 0 | 0 | 2 | 2 | 10 |
| 18 | 2024-10-26 | vs | LAC | L | 38 | 9 | Michael Porter | 4 | 13 | 30.8% | 0 | 6 | 4 | 7 | 1 | 1 | 2 | 7 | 9 | 3 | 2 | 0 | 1 | 2 | 3 | 13 |
| 19 | 2024-10-26 | vs | LAC | L | 16 | 8 | Julian Strawther | 3 | 5 | 60.0% | 2 | 2 | 1 | 3 | 0 | 0 | 1 | 1 | 2 | 3 | 2 | 0 | 1 | 5 | -12 | 12 |
| 20 | 2024-10-26 | vs | LAC | L | 19 | 2 | Russell Westbrook | 0 | 8 | 0.0% | 0 | 3 | 0 | 5 | 2 | 2 | 0 | 1 | 1 | 2 | 2 | 1 | 0 | 4 | -13 | 0 |
| 21 | 2024-10-26 | vs | LAC | L | 12 | 1 | Peyton Watson | 0 | 3 | 0.0% | 0 | 1 | 0 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | -7 | -2 |
| 22 | 2024-10-26 | vs | LAC | L | 11 | 0 | Dario Saric | 0 | 2 | 0.0% | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 2 | -12 | 0 |
| 23 | 2024-10-29 | @ | TOR | W | 44 | 40 | Nikola Jokic | 18 | 27 | 66.7% | 3 | 5 | 15 | 22 | 1 | 2 | 3 | 7 | 10 | 4 | 1 | 2 | 3 | 2 | 9 | 44 |
| 24 | 2024-10-29 | @ | TOR | W | 40 | 17 | Jamal Murray | 6 | 20 | 30.0% | 0 | 2 | 6 | 18 | 5 | 5 | 1 | 8 | 9 | 7 | 1 | 0 | 0 | 2 | 8 | 20 |
| 25 | 2024-10-29 | @ | TOR | W | 40 | 17 | Christian Braun | 6 | 11 | 54.5% | 1 | 4 | 5 | 7 | 4 | 4 | 2 | 2 | 4 | 2 | 1 | 1 | 1 | 3 | 8 | 19 |
| 26 | 2024-10-29 | @ | TOR | W | 42 | 16 | Aaron Gordon | 4 | 8 | 50.0% | 2 | 2 | 2 | 6 | 6 | 8 | 5 | 6 | 11 | 8 | 2 | 1 | 5 | 2 | 11 | 27 |
| 27 | 2024-10-29 | @ | TOR | W | 39 | 13 | Michael Porter | 6 | 12 | 50.0% | 1 | 4 | 5 | 8 | 0 | 1 | 2 | 7 | 9 | 2 | 0 | 1 | 1 | 3 | -10 | 17 |
| 28 | 2024-10-29 | @ | TOR | W | 18 | 9 | Russell Westbrook | 3 | 7 | 42.9% | 0 | 1 | 3 | 6 | 3 | 4 | 1 | 3 | 4 | 3 | 1 | 0 | 2 | 4 | -1 | 10 |
| 29 | 2024-10-29 | @ | TOR | W | 22 | 9 | Julian Strawther | 3 | 3 | 100.0% | 2 | 2 | 1 | 1 | 1 | 2 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 3 | 1 | 9 |
| 30 | 2024-10-29 | @ | TOR | W | 15 | 6 | Peyton Watson | 1 | 5 | 20.0% | 0 | 0 | 1 | 5 | 4 | 4 | 1 | 1 | 2 | 2 | 0 | 1 | 1 | 1 | -7 | 6 |
| 31 | 2024-10-29 | @ | TOR | W | 5 | 0 | Dario Saric | 0 | 3 | 0.0% | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 2 | 0 | -9 | -3 |
| 32 | 2024-10-30 | @ | BRK | W | 41 | 29 | Nikola Jokic | 9 | 16 | 56.3% | 0 | 0 | 9 | 16 | 11 | 13 | 6 | 12 | 18 | 16 | 0 | 1 | 1 | 3 | 8 | 54 |
| 33 | 2024-10-30 | @ | BRK | W | 33 | 24 | Aaron Gordon | 8 | 11 | 72.7% | 1 | 3 | 7 | 8 | 7 | 7 | 1 | 4 | 5 | 2 | 0 | 1 | 1 | 3 | 5 | 28 |
| 34 | 2024-10-30 | @ | BRK | W | 37 | 24 | Jamal Murray | 8 | 19 | 42.1% | 2 | 7 | 6 | 12 | 6 | 8 | 0 | 3 | 3 | 3 | 0 | 0 | 1 | 3 | 3 | 16 |
| 35 | 2024-10-30 | @ | BRK | W | 21 | 22 | Russell Westbrook | 5 | 12 | 41.7% | 2 | 2 | 3 | 10 | 10 | 10 | 0 | 1 | 1 | 5 | 1 | 0 | 1 | 2 | 0 | 21 |
| 36 | 2024-10-30 | @ | BRK | W | 41 | 16 | Michael Porter | 6 | 11 | 54.5% | 4 | 7 | 2 | 4 | 0 | 1 | 1 | 4 | 5 | 2 | 1 | 0 | 0 | 2 | 6 | 18 |
| 37 | 2024-10-30 | @ | BRK | W | 39 | 12 | Christian Braun | 3 | 7 | 42.9% | 2 | 3 | 1 | 4 | 4 | 6 | 0 | 3 | 3 | 3 | 0 | 2 | 0 | 4 | 9 | 14 |
| 38 | 2024-10-30 | @ | BRK | W | 22 | 7 | Julian Strawther | 3 | 6 | 50.0% | 1 | 3 | 2 | 3 | 0 | 0 | 0 | 4 | 4 | 0 | 1 | 0 | 1 | 0 | -3 | 8 |
| 39 | 2024-10-30 | @ | BRK | W | 20 | 7 | Peyton Watson | 3 | 5 | 60.0% | 1 | 2 | 2 | 3 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 5 | 0 | 7 |
| 40 | 2024-10-30 | @ | BRK | W | 12 | 3 | Dario Saric | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | -3 | 3 |
| 41 | 2024-11-02 | @ | MIN | L | 38 | 31 | Aaron Gordon | 11 | 18 | 61.1% | 5 | 7 | 6 | 11 | 4 | 5 | 4 | 7 | 11 | 2 | 1 | 0 | 0 | 2 | 8 | 37 |
| 42 | 2024-11-02 | @ | MIN | L | 40 | 26 | Nikola Jokic | 8 | 16 | 50.0% | 2 | 3 | 6 | 13 | 8 | 10 | 2 | 7 | 9 | 13 | 3 | 1 | 3 | 1 | 6 | 39 |
| 43 | 2024-11-02 | @ | MIN | L | 39 | 26 | Michael Porter | 11 | 18 | 61.1% | 3 | 7 | 8 | 11 | 1 | 2 | 2 | 4 | 6 | 4 | 2 | 0 | 3 | 2 | -5 | 27 |
| 44 | 2024-11-02 | @ | MIN | L | 36 | 14 | Christian Braun | 5 | 11 | 45.5% | 1 | 3 | 4 | 8 | 3 | 3 | 2 | 5 | 7 | 2 | 0 | 0 | 2 | 4 | 13 | 15 |
| 45 | 2024-11-02 | @ | MIN | L | 22 | 6 | Jamal Murray | 2 | 7 | 28.6% | 0 | 3 | 2 | 4 | 2 | 2 | 0 | 2 | 2 | 3 | 1 | 0 | 0 | 0 | 8 | 7 |
| 46 | 2024-11-02 | @ | MIN | L | 25 | 5 | Russell Westbrook | 1 | 8 | 12.5% | 0 | 3 | 1 | 5 | 3 | 4 | 3 | 3 | 6 | 5 | 2 | 1 | 4 | 2 | -13 | 7 |
| 47 | 2024-11-02 | @ | MIN | L | 16 | 4 | Julian Strawther | 1 | 4 | 25.0% | 0 | 2 | 1 | 2 | 2 | 3 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 5 | -13 | 2 |
| 48 | 2024-11-02 | @ | MIN | L | 6 | 2 | Hunter Tyson | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 3 |
| 49 | 2024-11-02 | @ | MIN | L | 14 | 2 | Peyton Watson | 1 | 6 | 16.7% | 0 | 2 | 1 | 4 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | -9 | -1 |
| 50 | 2024-11-02 | @ | MIN | L | 5 | 0 | Dario Saric | 0 | 3 | 0.0% | 0 | 2 | 0 | 1 | 0 | 0 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | -11 | -1 |
| 51 | 2024-11-03 | vs | UTAH | W | 30 | 27 | Nikola Jokic | 10 | 18 | 55.6% | 3 | 4 | 7 | 14 | 4 | 4 | 5 | 11 | 16 | 9 | 1 | 0 | 5 | 1 | 31 | 40 |
| 52 | 2024-11-03 | vs | UTAH | W | 29 | 20 | Michael Porter | 7 | 12 | 58.3% | 4 | 6 | 3 | 6 | 2 | 2 | 0 | 5 | 5 | 2 | 0 | 0 | 0 | 1 | 25 | 22 |
| 53 | 2024-11-03 | vs | UTAH | W | 19 | 19 | Julian Strawther | 7 | 11 | 63.6% | 3 | 6 | 4 | 5 | 2 | 2 | 1 | 1 | 2 | 0 | 2 | 0 | 1 | 5 | 12 | 18 |
| 54 | 2024-11-03 | vs | UTAH | W | 33 | 17 | Christian Braun | 6 | 11 | 54.5% | 3 | 5 | 3 | 6 | 2 | 2 | 0 | 2 | 2 | 3 | 2 | 0 | 1 | 3 | 21 | 18 |
| 55 | 2024-11-03 | vs | UTAH | W | 26 | 12 | Aaron Gordon | 5 | 9 | 55.6% | 2 | 3 | 3 | 6 | 0 | 0 | 2 | 4 | 6 | 5 | 0 | 0 | 0 | 2 | 28 | 19 |
| 56 | 2024-11-03 | vs | UTAH | W | 20 | 8 | Peyton Watson | 2 | 9 | 22.2% | 0 | 1 | 2 | 8 | 4 | 4 | 3 | 1 | 4 | 1 | 3 | 0 | 2 | 2 | -3 | 7 |
| 57 | 2024-11-03 | vs | UTAH | W | 25 | 7 | Hunter Tyson | 2 | 5 | 40.0% | 0 | 2 | 2 | 3 | 3 | 3 | 1 | 3 | 4 | 4 | 0 | 0 | 0 | 2 | 5 | 12 |
| 58 | 2024-11-03 | vs | UTAH | W | 4 | 6 | Zeke Nnaji | 3 | 3 | 100.0% | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 3 | 2 | 8 |
| 59 | 2024-11-03 | vs | UTAH | W | 14 | 6 | DeAndre Jordan | 3 | 7 | 42.9% | 0 | 0 | 3 | 7 | 0 | 2 | 6 | 3 | 9 | 1 | 0 | 0 | 4 | 2 | -7 | 6 |
| 60 | 2024-11-03 | vs | UTAH | W | 31 | 5 | Russell Westbrook | 2 | 11 | 18.2% | 1 | 4 | 1 | 7 | 0 | 4 | 1 | 2 | 3 | 7 | 5 | 0 | 4 | 4 | 12 | 3 |
| 61 | 2024-11-03 | vs | UTAH | W | 4 | 2 | Trey Alexander | 1 | 3 | 33.3% | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 62 | 2024-11-03 | vs | UTAH | W | 2 | 0 | Spencer Jones | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 2 |
| 63 | 2024-11-03 | vs | UTAH | W | 2 | 0 | Jalen Pickett | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 64 | 2024-11-05 | vs | TOR | W | 38 | 28 | Nikola Jokic | 10 | 22 | 45.5% | 1 | 5 | 9 | 17 | 7 | 8 | 5 | 9 | 14 | 13 | 1 | 2 | 7 | 2 | -1 | 38 |
| 65 | 2024-11-05 | vs | TOR | W | 34 | 21 | Russell Westbrook | 6 | 10 | 60.0% | 1 | 5 | 5 | 5 | 8 | 10 | 1 | 5 | 6 | 6 | 1 | 1 | 4 | 4 | -3 | 25 |
| 66 | 2024-11-05 | vs | TOR | W | 36 | 19 | Michael Porter | 7 | 16 | 43.8% | 3 | 10 | 4 | 6 | 2 | 3 | 4 | 5 | 9 | 3 | 1 | 1 | 2 | 2 | 1 | 21 |
| 67 | 2024-11-05 | vs | TOR | W | 36 | 17 | Christian Braun | 7 | 11 | 63.6% | 1 | 2 | 6 | 9 | 2 | 4 | 1 | 3 | 4 | 0 | 1 | 0 | 0 | 2 | 5 | 16 |
| 68 | 2024-11-05 | vs | TOR | W | 31 | 16 | Peyton Watson | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 7 | 9 | 1 | 2 | 3 | 1 | 2 | 2 | 1 | 5 | 9 | 18 |
| 69 | 2024-11-05 | vs | TOR | W | 26 | 13 | Julian Strawther | 4 | 10 | 40.0% | 2 | 4 | 2 | 6 | 3 | 4 | 0 | 2 | 2 | 3 | 2 | 1 | 1 | 2 | -1 | 13 |
| 70 | 2024-11-05 | vs | TOR | W | 4 | 3 | Aaron Gordon | 1 | 2 | 50.0% | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 2 |
| 71 | 2024-11-05 | vs | TOR | W | 6 | 2 | DeAndre Jordan | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 3 | 4 | 1 | 0 | 1 | 1 | 1 | 3 | 7 |
| 72 | 2024-11-05 | vs | TOR | W | 14 | 2 | Zeke Nnaji | 1 | 3 | 33.3% | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | -2 | 3 |
| 73 | 2024-11-05 | vs | TOR | W | 15 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 2 | -2 | 2 |
| 74 | 2024-11-07 | vs | OKC | W | 32 | 29 | Russell Westbrook | 10 | 15 | 66.7% | 3 | 4 | 7 | 11 | 6 | 9 | 1 | 5 | 6 | 6 | 1 | 0 | 4 | 4 | -14 | 30 |
| 75 | 2024-11-07 | vs | OKC | W | 37 | 24 | Christian Braun | 7 | 14 | 50.0% | 4 | 8 | 3 | 6 | 6 | 6 | 1 | 7 | 8 | 0 | 2 | 1 | 2 | 2 | 6 | 26 |
| 76 | 2024-11-07 | vs | OKC | W | 41 | 24 | Michael Porter | 7 | 16 | 43.8% | 6 | 10 | 1 | 6 | 4 | 4 | 1 | 6 | 7 | 3 | 1 | 0 | 1 | 3 | 4 | 25 |
| 77 | 2024-11-07 | vs | OKC | W | 39 | 23 | Nikola Jokic | 9 | 20 | 45.0% | 1 | 3 | 8 | 17 | 4 | 6 | 7 | 13 | 20 | 16 | 2 | 2 | 5 | 2 | 8 | 45 |
| 78 | 2024-11-07 | vs | OKC | W | 34 | 10 | Peyton Watson | 4 | 9 | 44.4% | 1 | 4 | 3 | 5 | 1 | 4 | 0 | 3 | 3 | 3 | 1 | 3 | 2 | 3 | 7 | 10 |
| 79 | 2024-11-07 | vs | OKC | W | 27 | 9 | Julian Strawther | 3 | 11 | 27.3% | 1 | 7 | 2 | 4 | 2 | 2 | 2 | 3 | 5 | 6 | 0 | 0 | 2 | 4 | 12 | 10 |
| 80 | 2024-11-07 | vs | OKC | W | 10 | 3 | Zeke Nnaji | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | -7 | 3 |
| 81 | 2024-11-07 | vs | OKC | W | 19 | 2 | Hunter Tyson | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 4 | 4 | 0 | 1 | 0 | 2 | 4 | -6 | 4 |
| 82 | 2024-11-09 | vs | MIA | W | 40 | 30 | Nikola Jokic | 11 | 13 | 84.6% | 1 | 1 | 10 | 12 | 7 | 7 | 2 | 9 | 11 | 14 | 2 | 0 | 5 | 2 | 26 | 50 |
| 83 | 2024-11-09 | vs | MIA | W | 35 | 28 | Jamal Murray | 9 | 17 | 52.9% | 4 | 10 | 5 | 7 | 6 | 6 | 0 | 4 | 4 | 6 | 0 | 0 | 4 | 0 | 28 | 26 |
| 84 | 2024-11-09 | vs | MIA | W | 35 | 21 | Christian Braun | 7 | 9 | 77.8% | 3 | 4 | 4 | 5 | 4 | 6 | 1 | 5 | 6 | 2 | 0 | 1 | 1 | 1 | 14 | 25 |
| 85 | 2024-11-09 | vs | MIA | W | 36 | 21 | Michael Porter | 8 | 15 | 53.3% | 5 | 10 | 3 | 5 | 0 | 0 | 0 | 5 | 5 | 4 | 2 | 1 | 2 | 2 | 24 | 24 |
| 86 | 2024-11-09 | vs | MIA | W | 33 | 16 | Peyton Watson | 7 | 11 | 63.6% | 2 | 3 | 5 | 8 | 0 | 0 | 2 | 3 | 5 | 1 | 1 | 1 | 0 | 3 | 12 | 20 |
| 87 | 2024-11-09 | vs | MIA | W | 9 | 7 | Hunter Tyson | 3 | 3 | 100.0% | 1 | 1 | 2 | 2 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 1 | 2 | -12 | 8 |
| 88 | 2024-11-09 | vs | MIA | W | 24 | 6 | Russell Westbrook | 2 | 5 | 40.0% | 2 | 3 | 0 | 2 | 0 | 0 | 0 | 4 | 4 | 10 | 0 | 1 | 2 | 2 | -5 | 16 |
| 89 | 2024-11-09 | vs | MIA | W | 24 | 6 | Julian Strawther | 3 | 8 | 37.5% | 0 | 3 | 3 | 5 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 2 | 1 | -12 | 1 |
| 90 | 2024-11-09 | vs | MIA | W | 3 | 0 | Zeke Nnaji | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | -10 | 1 |
| 91 | 2024-11-11 | vs | DAL | W | 38 | 37 | Nikola Jokic | 13 | 21 | 61.9% | 3 | 3 | 10 | 18 | 8 | 8 | 8 | 10 | 18 | 15 | 3 | 0 | 4 | 2 | 13 | 61 |
| 92 | 2024-11-11 | vs | DAL | W | 38 | 18 | Jamal Murray | 7 | 17 | 41.2% | 2 | 6 | 5 | 11 | 2 | 5 | 0 | 2 | 2 | 6 | 0 | 0 | 2 | 3 | 15 | 11 |
| 93 | 2024-11-11 | vs | DAL | W | 36 | 17 | Michael Porter | 6 | 11 | 54.5% | 2 | 6 | 4 | 5 | 3 | 4 | 0 | 7 | 7 | 4 | 1 | 2 | 0 | 2 | 2 | 25 |
| 94 | 2024-11-11 | vs | DAL | W | 40 | 16 | Peyton Watson | 6 | 9 | 66.7% | 4 | 4 | 2 | 5 | 0 | 0 | 1 | 4 | 5 | 1 | 0 | 1 | 0 | 1 | 3 | 20 |
| 95 | 2024-11-11 | vs | DAL | W | 35 | 14 | Christian Braun | 5 | 10 | 50.0% | 1 | 2 | 4 | 8 | 3 | 4 | 2 | 4 | 6 | 2 | 0 | 0 | 1 | 3 | 8 | 15 |
| 96 | 2024-11-11 | vs | DAL | W | 23 | 12 | Julian Strawther | 4 | 9 | 44.4% | 2 | 5 | 2 | 4 | 2 | 3 | 0 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | -7 | 10 |
| 97 | 2024-11-11 | vs | DAL | W | 20 | 6 | Russell Westbrook | 3 | 10 | 30.0% | 0 | 2 | 3 | 8 | 0 | 0 | 2 | 2 | 4 | 5 | 1 | 0 | 2 | 2 | -13 | 7 |
| 98 | 2024-11-11 | vs | DAL | W | 10 | 2 | Zeke Nnaji | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 2 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 1 | -11 | 2 |
| 99 | 2024-11-16 | @ | NOP | L | 39 | 24 | Michael Porter | 10 | 18 | 55.6% | 4 | 7 | 6 | 11 | 0 | 0 | 1 | 5 | 6 | 3 | 0 | 0 | 0 | 3 | 1 | 25 |
| 100 | 2024-11-16 | @ | NOP | L | 29 | 18 | Peyton Watson | 6 | 12 | 50.0% | 1 | 5 | 5 | 7 | 5 | 6 | 0 | 5 | 5 | 0 | 1 | 0 | 0 | 1 | -4 | 17 |
| 101 | 2024-11-16 | @ | NOP | L | 43 | 16 | Jamal Murray | 6 | 16 | 37.5% | 2 | 8 | 4 | 8 | 2 | 2 | 0 | 6 | 6 | 8 | 2 | 0 | 0 | 1 | 0 | 22 |
| 102 | 2024-11-16 | @ | NOP | L | 37 | 15 | Christian Braun | 7 | 12 | 58.3% | 0 | 2 | 7 | 10 | 1 | 1 | 1 | 3 | 4 | 3 | 5 | 0 | 1 | 3 | -11 | 21 |
| 103 | 2024-11-16 | @ | NOP | L | 34 | 9 | Dario Saric | 4 | 9 | 44.4% | 1 | 5 | 3 | 4 | 0 | 0 | 1 | 7 | 8 | 5 | 1 | 0 | 4 | 4 | 12 | 14 |
| 104 | 2024-11-16 | @ | NOP | L | 22 | 5 | Russell Westbrook | 2 | 8 | 25.0% | 1 | 3 | 1 | 5 | 0 | 0 | 1 | 3 | 4 | 7 | 0 | 0 | 1 | 3 | -1 | 9 |
| 105 | 2024-11-16 | @ | NOP | L | 20 | 5 | Julian Strawther | 1 | 6 | 16.7% | 1 | 4 | 0 | 2 | 2 | 2 | 0 | 3 | 3 | 0 | 0 | 0 | 1 | 1 | -11 | 2 |
| 106 | 2024-11-16 | @ | NOP | L | 8 | 2 | Vlatko Cancar | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | 4 |
| 107 | 2024-11-16 | @ | NOP | L | 6 | 0 | Zeke Nnaji | 0 | 2 | 0.0% | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | -15 | -1 |
| 108 | 2024-11-16 | @ | NOP | L | 2 | 0 | Hunter Tyson | 0 | 2 | 0.0% | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -7 | -2 |
| 109 | 2024-11-18 | @ | MEM | L | 25 | 19 | Julian Strawther | 6 | 13 | 46.2% | 4 | 7 | 2 | 6 | 3 | 4 | 0 | 4 | 4 | 3 | 1 | 1 | 0 | 2 | -11 | 20 |
| 110 | 2024-11-18 | @ | MEM | L | 37 | 13 | Jamal Murray | 6 | 15 | 40.0% | 1 | 6 | 5 | 9 | 0 | 0 | 1 | 5 | 6 | 7 | 3 | 2 | 6 | 3 | -3 | 16 |
| 111 | 2024-11-18 | @ | MEM | L | 34 | 13 | Christian Braun | 5 | 11 | 45.5% | 0 | 2 | 5 | 9 | 3 | 4 | 1 | 3 | 4 | 4 | 1 | 0 | 1 | 0 | -2 | 14 |
| 112 | 2024-11-18 | @ | MEM | L | 23 | 12 | Russell Westbrook | 4 | 10 | 40.0% | 2 | 3 | 2 | 7 | 2 | 4 | 0 | 3 | 3 | 3 | 1 | 1 | 3 | 1 | -10 | 9 |
| 113 | 2024-11-18 | @ | MEM | L | 32 | 10 | Dario Saric | 4 | 10 | 40.0% | 2 | 5 | 2 | 5 | 0 | 0 | 2 | 8 | 10 | 3 | 2 | 0 | 0 | 1 | -5 | 19 |
| 114 | 2024-11-18 | @ | MEM | L | 27 | 10 | Michael Porter | 4 | 12 | 33.3% | 0 | 4 | 4 | 8 | 2 | 2 | 0 | 3 | 3 | 1 | 0 | 0 | 2 | 1 | -15 | 4 |
| 115 | 2024-11-18 | @ | MEM | L | 28 | 7 | Peyton Watson | 3 | 7 | 42.9% | 1 | 2 | 2 | 5 | 0 | 0 | 1 | 4 | 5 | 2 | 1 | 1 | 0 | 0 | -10 | 12 |
| 116 | 2024-11-18 | @ | MEM | L | 14 | 4 | DeAndre Jordan | 2 | 3 | 66.7% | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | -10 | 5 |
| 117 | 2024-11-18 | @ | MEM | L | 13 | 2 | Vlatko Cancar | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 3 | -9 | 1 |
| 118 | 2024-11-18 | @ | MEM | L | 1 | 0 | Zeke Nnaji | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 119 | 2024-11-18 | @ | MEM | L | 1 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 120 | 2024-11-18 | @ | MEM | L | 1 | 0 | P.J. Hall | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 121 | 2024-11-18 | @ | MEM | L | 1 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 122 | 2024-11-20 | @ | MEM | W | 36 | 27 | Jamal Murray | 10 | 20 | 50.0% | 5 | 10 | 5 | 10 | 2 | 3 | 1 | 3 | 4 | 6 | 3 | 2 | 3 | 2 | 18 | 28 |
| 123 | 2024-11-20 | @ | MEM | W | 40 | 24 | Michael Porter | 11 | 21 | 52.4% | 0 | 4 | 11 | 17 | 2 | 6 | 5 | 6 | 11 | 3 | 1 | 1 | 0 | 3 | 15 | 26 |
| 124 | 2024-11-20 | @ | MEM | W | 34 | 19 | Christian Braun | 7 | 12 | 58.3% | 0 | 2 | 7 | 10 | 5 | 6 | 2 | 5 | 7 | 2 | 3 | 0 | 2 | 4 | 22 | 23 |
| 125 | 2024-11-20 | @ | MEM | W | 25 | 15 | Peyton Watson | 6 | 10 | 60.0% | 2 | 5 | 4 | 5 | 1 | 1 | 1 | 4 | 5 | 2 | 1 | 1 | 4 | 6 | 8 | 16 |
| 126 | 2024-11-20 | @ | MEM | W | 32 | 12 | Russell Westbrook | 5 | 12 | 41.7% | 1 | 4 | 4 | 8 | 1 | 2 | 1 | 9 | 10 | 14 | 2 | 1 | 5 | 3 | -2 | 26 |
| 127 | 2024-11-20 | @ | MEM | W | 18 | 8 | Dario Saric | 2 | 6 | 33.3% | 2 | 4 | 0 | 2 | 2 | 2 | 0 | 5 | 5 | 1 | 2 | 0 | 3 | 5 | 10 | 9 |
| 128 | 2024-11-20 | @ | MEM | W | 20 | 6 | Julian Strawther | 2 | 7 | 28.6% | 1 | 4 | 1 | 3 | 1 | 2 | 0 | 5 | 5 | 0 | 2 | 1 | 0 | 3 | -6 | 8 |
| 129 | 2024-11-20 | @ | MEM | W | 11 | 5 | Vlatko Cancar | 2 | 3 | 66.7% | 1 | 1 | 1 | 2 | 0 | 0 | 1 | 3 | 4 | 0 | 0 | 0 | 1 | 1 | 6 | 7 |
| 130 | 2024-11-20 | @ | MEM | W | 8 | 4 | DeAndre Jordan | 2 | 4 | 50.0% | 0 | 0 | 2 | 4 | 0 | 0 | 2 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 5 | 5 |
| 131 | 2024-11-20 | @ | MEM | W | 6 | 2 | Zeke Nnaji | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -7 | 1 |
| 132 | 2024-11-20 | @ | MEM | W | 9 | 0 | Trey Alexander | 0 | 3 | 0.0% | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | -9 | -2 |
| 133 | 2024-11-23 | vs | DAL | L | 39 | 33 | Nikola Jokic | 13 | 22 | 59.1% | 2 | 2 | 11 | 20 | 5 | 7 | 6 | 11 | 17 | 10 | 0 | 0 | 3 | 0 | 4 | 46 |
| 134 | 2024-11-23 | vs | DAL | L | 36 | 17 | Michael Porter | 7 | 12 | 58.3% | 2 | 3 | 5 | 9 | 1 | 2 | 1 | 4 | 5 | 5 | 0 | 0 | 1 | 4 | -7 | 20 |
| 135 | 2024-11-23 | vs | DAL | L | 29 | 17 | Christian Braun | 6 | 9 | 66.7% | 2 | 3 | 4 | 6 | 3 | 6 | 4 | 1 | 5 | 0 | 1 | 1 | 2 | 2 | -9 | 16 |
| 136 | 2024-11-23 | vs | DAL | L | 27 | 16 | Russell Westbrook | 5 | 13 | 38.5% | 4 | 6 | 1 | 7 | 2 | 5 | 0 | 1 | 1 | 4 | 2 | 0 | 1 | 2 | 0 | 11 |
| 137 | 2024-11-23 | vs | DAL | L | 40 | 15 | Peyton Watson | 7 | 11 | 63.6% | 1 | 3 | 6 | 8 | 0 | 0 | 1 | 4 | 5 | 3 | 0 | 3 | 2 | 3 | 2 | 20 |
| 138 | 2024-11-23 | vs | DAL | L | 39 | 14 | Jamal Murray | 4 | 16 | 25.0% | 4 | 11 | 0 | 5 | 2 | 2 | 0 | 5 | 5 | 11 | 0 | 3 | 3 | 3 | 0 | 18 |
| 139 | 2024-11-23 | vs | DAL | L | 5 | 3 | DeAndre Jordan | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 1 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | -2 | 2 |
| 140 | 2024-11-23 | vs | DAL | L | 17 | 3 | Julian Strawther | 0 | 2 | 0.0% | 0 | 0 | 0 | 2 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | -1 | 2 |
| 141 | 2024-11-23 | vs | DAL | L | 4 | 2 | Dario Saric | 0 | 2 | 0.0% | 0 | 1 | 0 | 1 | 2 | 2 | 2 | 1 | 3 | 0 | 0 | 0 | 1 | 1 | -7 | 2 |
| 142 | 2024-11-23 | vs | DAL | L | 2 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 |
| 143 | 2024-11-23 | vs | DAL | L | 2 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 | -1 |
| 144 | 2024-11-24 | @ | LAL | W | 37 | 34 | Nikola Jokic | 12 | 20 | 60.0% | 3 | 7 | 9 | 13 | 7 | 8 | 2 | 11 | 13 | 8 | 2 | 1 | 6 | 3 | 39 | 43 |
| 145 | 2024-11-24 | @ | LAL | W | 36 | 24 | Michael Porter | 10 | 15 | 66.7% | 4 | 7 | 6 | 8 | 0 | 1 | 1 | 10 | 11 | 4 | 1 | 0 | 2 | 2 | 30 | 32 |
| 146 | 2024-11-24 | @ | LAL | W | 31 | 16 | Christian Braun | 7 | 7 | 100.0% | 2 | 2 | 5 | 5 | 0 | 0 | 0 | 2 | 2 | 3 | 1 | 0 | 2 | 2 | 13 | 20 |
| 147 | 2024-11-24 | @ | LAL | W | 24 | 14 | Russell Westbrook | 6 | 10 | 60.0% | 1 | 4 | 5 | 6 | 1 | 2 | 2 | 5 | 7 | 11 | 1 | 0 | 3 | 3 | 17 | 25 |
| 148 | 2024-11-24 | @ | LAL | W | 30 | 14 | Jamal Murray | 5 | 12 | 41.7% | 2 | 4 | 3 | 8 | 2 | 6 | 0 | 5 | 5 | 5 | 1 | 0 | 2 | 2 | 20 | 12 |
| 149 | 2024-11-24 | @ | LAL | W | 35 | 11 | Peyton Watson | 5 | 8 | 62.5% | 1 | 1 | 4 | 7 | 0 | 0 | 2 | 3 | 5 | 2 | 0 | 1 | 1 | 1 | 28 | 15 |
| 150 | 2024-11-24 | @ | LAL | W | 19 | 6 | Julian Strawther | 2 | 3 | 66.7% | 2 | 2 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 1 | 0 | 0 | 0 | 3 | -12 | 8 |
| 151 | 2024-11-24 | @ | LAL | W | 10 | 3 | Trey Alexander | 1 | 2 | 50.0% | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 8 | 2 |
| 152 | 2024-11-24 | @ | LAL | W | 5 | 2 | DeAndre Jordan | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 4 |
| 153 | 2024-11-24 | @ | LAL | W | 3 | 2 | P.J. Hall | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -2 | 1 |
| 154 | 2024-11-24 | @ | LAL | W | 3 | 1 | Hunter Tyson | 0 | 0 | - | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | -2 | 3 |
| 155 | 2024-11-24 | @ | LAL | W | 4 | 0 | Dario Saric | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | -12 | 0 |
| 156 | 2024-11-24 | @ | LAL | W | 3 | 0 | Zeke Nnaji | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2 | -1 |
| 157 | 2024-11-26 | vs | NYK | L | 26 | 27 | Russell Westbrook | 9 | 16 | 56.3% | 4 | 7 | 5 | 9 | 5 | 5 | 1 | 2 | 3 | 3 | 2 | 1 | 3 | 1 | -16 | 26 |
| 158 | 2024-11-26 | vs | NYK | L | 32 | 22 | Nikola Jokic | 9 | 20 | 45.0% | 2 | 7 | 7 | 13 | 2 | 2 | 3 | 4 | 7 | 7 | 1 | 0 | 0 | 1 | -20 | 26 |
| 159 | 2024-11-26 | vs | NYK | L | 31 | 20 | Jamal Murray | 6 | 13 | 46.2% | 1 | 5 | 5 | 8 | 7 | 9 | 1 | 3 | 4 | 7 | 1 | 0 | 0 | 1 | -13 | 23 |
| 160 | 2024-11-26 | vs | NYK | L | 34 | 18 | Michael Porter | 7 | 17 | 41.2% | 3 | 7 | 4 | 10 | 1 | 1 | 4 | 6 | 10 | 0 | 0 | 0 | 2 | 4 | -13 | 16 |
| 161 | 2024-11-26 | vs | NYK | L | 32 | 14 | Christian Braun | 5 | 10 | 50.0% | 0 | 0 | 5 | 10 | 4 | 4 | 0 | 1 | 1 | 2 | 1 | 1 | 1 | 0 | -13 | 13 |
| 162 | 2024-11-26 | vs | NYK | L | 22 | 6 | Peyton Watson | 2 | 4 | 50.0% | 0 | 1 | 2 | 3 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | -18 | 3 |
| 163 | 2024-11-26 | vs | NYK | L | 10 | 5 | Hunter Tyson | 2 | 3 | 66.7% | 1 | 2 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | -4 | 6 |
| 164 | 2024-11-26 | vs | NYK | L | 23 | 4 | Julian Strawther | 1 | 4 | 25.0% | 0 | 1 | 1 | 3 | 2 | 2 | 0 | 2 | 2 | 1 | 0 | 0 | 1 | 3 | -20 | 3 |
| 165 | 2024-11-26 | vs | NYK | L | 11 | 2 | Trey Alexander | 0 | 2 | 0.0% | 0 | 2 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | -8 | 1 |
| 166 | 2024-11-26 | vs | NYK | L | 14 | 0 | DeAndre Jordan | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 1 | 0 | 0 | 2 | 4 | -5 | 4 |
| 167 | 2024-11-26 | vs | NYK | L | 2 | 0 | P.J. Hall | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -2 | 0 |
| 168 | 2024-11-26 | vs | NYK | L | 3 | 0 | Zeke Nnaji | 0 | 3 | 0.0% | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -3 | -3 |
| 169 | 2024-11-28 | @ | UTAH | W | 34 | 30 | Nikola Jokic | 13 | 19 | 68.4% | 2 | 3 | 11 | 16 | 2 | 3 | 3 | 7 | 10 | 7 | 1 | 1 | 1 | 1 | 20 | 41 |
| 170 | 2024-11-28 | @ | UTAH | W | 36 | 22 | Jamal Murray | 10 | 18 | 55.6% | 1 | 6 | 9 | 12 | 1 | 1 | 2 | 2 | 4 | 8 | 4 | 0 | 1 | 2 | 18 | 29 |
| 171 | 2024-11-28 | @ | UTAH | W | 31 | 19 | Michael Porter | 8 | 15 | 53.3% | 2 | 6 | 6 | 9 | 1 | 1 | 4 | 3 | 7 | 4 | 1 | 0 | 1 | 2 | 17 | 23 |
| 172 | 2024-11-28 | @ | UTAH | W | 41 | 18 | Christian Braun | 6 | 8 | 75.0% | 2 | 3 | 4 | 5 | 4 | 4 | 0 | 7 | 7 | 3 | 0 | 1 | 1 | 1 | 18 | 26 |
| 173 | 2024-11-28 | @ | UTAH | W | 19 | 10 | Russell Westbrook | 3 | 9 | 33.3% | 1 | 4 | 2 | 5 | 3 | 4 | 0 | 1 | 1 | 5 | 2 | 1 | 3 | 0 | 6 | 9 |
| 174 | 2024-11-28 | @ | UTAH | W | 28 | 9 | Peyton Watson | 3 | 6 | 50.0% | 0 | 3 | 3 | 3 | 3 | 4 | 1 | 2 | 3 | 2 | 1 | 1 | 0 | 1 | 20 | 12 |
| 175 | 2024-11-28 | @ | UTAH | W | 15 | 9 | Julian Strawther | 2 | 4 | 50.0% | 2 | 4 | 0 | 0 | 3 | 4 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 4 | 2 | 7 |
| 176 | 2024-11-28 | @ | UTAH | W | 16 | 5 | Hunter Tyson | 1 | 3 | 33.3% | 1 | 2 | 0 | 1 | 2 | 2 | 0 | 4 | 4 | 0 | 0 | 2 | 2 | 2 | -5 | 7 |
| 177 | 2024-11-28 | @ | UTAH | W | 13 | 0 | DeAndre Jordan | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 1 | 0 | 1 | -1 | 4 |
| 178 | 2024-11-28 | @ | UTAH | W | 2 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 179 | 2024-11-28 | @ | UTAH | W | 2 | 0 | Zeke Nnaji | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 180 | 2024-11-28 | @ | UTAH | W | 2 | 0 | Spencer Jones | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 181 | 2024-11-28 | @ | UTAH | W | 2 | 0 | P.J. Hall | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -1 |
| 182 | 2024-12-02 | @ | LAC | L | 39 | 28 | Nikola Jokic | 12 | 24 | 50.0% | 2 | 7 | 10 | 17 | 2 | 4 | 3 | 11 | 14 | 11 | 1 | 1 | 5 | 3 | -7 | 36 |
| 183 | 2024-12-02 | @ | LAC | L | 32 | 18 | Michael Porter | 8 | 11 | 72.7% | 1 | 1 | 7 | 10 | 1 | 4 | 0 | 7 | 7 | 1 | 0 | 0 | 1 | 3 | 3 | 19 |
| 184 | 2024-12-02 | @ | LAC | L | 38 | 18 | Jamal Murray | 7 | 12 | 58.3% | 2 | 4 | 5 | 8 | 2 | 2 | 0 | 0 | 0 | 7 | 1 | 0 | 3 | 1 | -1 | 18 |
| 185 | 2024-12-02 | @ | LAC | L | 19 | 15 | Julian Strawther | 6 | 7 | 85.7% | 2 | 3 | 4 | 4 | 1 | 1 | 1 | 1 | 2 | 2 | 0 | 0 | 1 | 2 | -2 | 17 |
| 186 | 2024-12-02 | @ | LAC | L | 29 | 13 | Peyton Watson | 6 | 9 | 66.7% | 1 | 2 | 5 | 7 | 0 | 0 | 2 | 3 | 5 | 2 | 1 | 1 | 1 | 2 | 1 | 18 |
| 187 | 2024-12-02 | @ | LAC | L | 36 | 11 | Christian Braun | 5 | 7 | 71.4% | 0 | 0 | 5 | 7 | 1 | 3 | 2 | 5 | 7 | 2 | 1 | 0 | 2 | 2 | 0 | 15 |
| 188 | 2024-12-02 | @ | LAC | L | 24 | 10 | Aaron Gordon | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 1 | 1 | 0 | 1 | 1 | 4 | 1 | 0 | 1 | 2 | -7 | 12 |
| 189 | 2024-12-02 | @ | LAC | L | 25 | 9 | Russell Westbrook | 3 | 11 | 27.3% | 1 | 5 | 2 | 6 | 2 | 4 | 0 | 3 | 3 | 8 | 2 | 1 | 0 | 2 | -7 | 13 |
| 190 | 2024-12-04 | vs | GSW | W | 40 | 38 | Nikola Jokic | 14 | 24 | 58.3% | 3 | 4 | 11 | 20 | 7 | 9 | 2 | 8 | 10 | 6 | 5 | 1 | 4 | 1 | 23 | 44 |
| 191 | 2024-12-04 | vs | GSW | W | 31 | 22 | Michael Porter | 8 | 14 | 57.1% | 1 | 3 | 7 | 11 | 5 | 6 | 1 | 6 | 7 | 2 | 1 | 0 | 2 | 2 | -1 | 23 |
| 192 | 2024-12-04 | vs | GSW | W | 33 | 15 | Aaron Gordon | 4 | 6 | 66.7% | 3 | 4 | 1 | 2 | 4 | 6 | 0 | 9 | 9 | 5 | 0 | 0 | 2 | 0 | 13 | 23 |
| 193 | 2024-12-04 | vs | GSW | W | 34 | 12 | Jamal Murray | 4 | 12 | 33.3% | 1 | 3 | 3 | 9 | 3 | 3 | 0 | 3 | 3 | 7 | 1 | 3 | 1 | 3 | 4 | 17 |
| 194 | 2024-12-04 | vs | GSW | W | 36 | 11 | Christian Braun | 3 | 7 | 42.9% | 0 | 2 | 3 | 5 | 5 | 7 | 0 | 6 | 6 | 2 | 1 | 0 | 3 | 3 | 23 | 11 |
| 195 | 2024-12-04 | vs | GSW | W | 18 | 7 | Peyton Watson | 1 | 1 | 100.0% | 1 | 1 | 0 | 0 | 4 | 4 | 1 | 0 | 1 | 2 | 0 | 1 | 1 | 1 | -17 | 10 |
| 196 | 2024-12-04 | vs | GSW | W | 23 | 7 | Russell Westbrook | 3 | 7 | 42.9% | 1 | 3 | 2 | 4 | 0 | 0 | 0 | 1 | 1 | 5 | 0 | 0 | 1 | 3 | -10 | 8 |
| 197 | 2024-12-04 | vs | GSW | W | 20 | 5 | Julian Strawther | 2 | 7 | 28.6% | 1 | 4 | 1 | 3 | 0 | 0 | 0 | 1 | 1 | 3 | 1 | 0 | 1 | 1 | -4 | 4 |
| 198 | 2024-12-04 | vs | GSW | W | 5 | 2 | Zeke Nnaji | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -11 | 2 |
| 199 | 2024-12-06 | @ | CLE | L | 39 | 27 | Nikola Jokic | 13 | 26 | 50.0% | 0 | 3 | 13 | 23 | 1 | 1 | 3 | 17 | 20 | 11 | 3 | 0 | 3 | 3 | -8 | 45 |
| 200 | 2024-12-06 | @ | CLE | L | 38 | 24 | Michael Porter | 8 | 15 | 53.3% | 3 | 6 | 5 | 9 | 5 | 6 | 2 | 5 | 7 | 2 | 4 | 2 | 4 | 0 | 1 | 27 |
| 201 | 2024-12-06 | @ | CLE | L | 42 | 19 | Jamal Murray | 7 | 16 | 43.8% | 1 | 3 | 6 | 13 | 4 | 5 | 1 | 3 | 4 | 6 | 1 | 0 | 1 | 0 | -10 | 19 |
| 202 | 2024-12-06 | @ | CLE | L | 34 | 18 | Aaron Gordon | 8 | 13 | 61.5% | 0 | 2 | 8 | 11 | 2 | 4 | 2 | 5 | 7 | 2 | 0 | 0 | 2 | 1 | 1 | 18 |
| 203 | 2024-12-06 | @ | CLE | L | 16 | 10 | Russell Westbrook | 4 | 9 | 44.4% | 1 | 3 | 3 | 6 | 1 | 2 | 1 | 2 | 3 | 3 | 1 | 1 | 2 | 1 | -15 | 10 |
| 204 | 2024-12-06 | @ | CLE | L | 39 | 10 | Christian Braun | 4 | 10 | 40.0% | 0 | 3 | 4 | 7 | 2 | 2 | 0 | 4 | 4 | 0 | 1 | 2 | 2 | 2 | 2 | 9 |
| 205 | 2024-12-06 | @ | CLE | L | 18 | 4 | Peyton Watson | 1 | 3 | 33.3% | 1 | 2 | 0 | 1 | 1 | 2 | 0 | 4 | 4 | 1 | 2 | 0 | 1 | 3 | -13 | 7 |
| 206 | 2024-12-06 | @ | CLE | L | 12 | 2 | Julian Strawther | 1 | 4 | 25.0% | 0 | 2 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | -14 | -1 |
| 207 | 2024-12-06 | @ | CLE | L | 3 | 0 | Zeke Nnaji | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -4 | 0 |
| 208 | 2024-12-08 | @ | WAS | L | 39 | 56 | Nikola Jokic | 22 | 38 | 57.9% | 3 | 5 | 19 | 33 | 9 | 13 | 7 | 9 | 16 | 8 | 1 | 0 | 5 | 5 | -1 | 56 |
| 209 | 2024-12-08 | @ | WAS | L | 33 | 18 | Julian Strawther | 7 | 13 | 53.8% | 2 | 5 | 5 | 8 | 2 | 2 | 1 | 2 | 3 | 2 | 3 | 2 | 1 | 3 | 4 | 21 |
| 210 | 2024-12-08 | @ | WAS | L | 38 | 14 | Christian Braun | 7 | 14 | 50.0% | 0 | 3 | 7 | 11 | 0 | 0 | 2 | 0 | 2 | 0 | 1 | 1 | 2 | 3 | -1 | 9 |
| 211 | 2024-12-08 | @ | WAS | L | 36 | 11 | Michael Porter | 5 | 14 | 35.7% | 0 | 5 | 5 | 9 | 1 | 2 | 1 | 4 | 5 | 6 | 1 | 1 | 2 | 2 | -10 | 12 |
| 212 | 2024-12-08 | @ | WAS | L | 32 | 7 | Russell Westbrook | 3 | 6 | 50.0% | 0 | 2 | 3 | 4 | 1 | 4 | 1 | 9 | 10 | 12 | 4 | 1 | 2 | 5 | -9 | 26 |
| 213 | 2024-12-08 | @ | WAS | L | 26 | 4 | Peyton Watson | 2 | 5 | 40.0% | 0 | 1 | 2 | 4 | 0 | 0 | 0 | 2 | 2 | 2 | 1 | 1 | 1 | 3 | -17 | 6 |
| 214 | 2024-12-08 | @ | WAS | L | 12 | 2 | Hunter Tyson | 0 | 2 | 0.0% | 0 | 2 | 0 | 0 | 2 | 2 | 2 | 2 | 4 | 0 | 0 | 0 | 0 | 0 | -5 | 4 |
| 215 | 2024-12-08 | @ | WAS | L | 9 | 1 | DeAndre Jordan | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 1 | 4 | 1 | 2 | 3 | 2 | 0 | 0 | 2 | 0 | -6 | 0 |
| 216 | 2024-12-08 | @ | WAS | L | 16 | 0 | Jalen Pickett | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 3 | 2 | 0 | 0 | 0 | 2 | 0 | 4 |
| 217 | 2024-12-09 | @ | ATL | W | 35 | 48 | Nikola Jokic | 17 | 29 | 58.6% | 3 | 6 | 14 | 23 | 11 | 13 | 3 | 11 | 14 | 8 | 3 | 0 | 3 | 2 | 25 | 56 |
| 218 | 2024-12-09 | @ | ATL | W | 36 | 26 | Michael Porter | 12 | 17 | 70.6% | 1 | 5 | 11 | 12 | 1 | 1 | 2 | 5 | 7 | 3 | 0 | 0 | 1 | 2 | 15 | 30 |
| 219 | 2024-12-09 | @ | ATL | W | 32 | 17 | Christian Braun | 7 | 11 | 63.6% | 2 | 2 | 5 | 9 | 1 | 2 | 2 | 6 | 8 | 4 | 2 | 1 | 1 | 3 | 34 | 26 |
| 220 | 2024-12-09 | @ | ATL | W | 22 | 13 | Julian Strawther | 5 | 6 | 83.3% | 3 | 4 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 3 | 3 | -2 | 14 |
| 221 | 2024-12-09 | @ | ATL | W | 31 | 9 | Russell Westbrook | 4 | 6 | 66.7% | 1 | 3 | 3 | 3 | 0 | 1 | 0 | 1 | 1 | 11 | 2 | 1 | 4 | 3 | 12 | 17 |
| 222 | 2024-12-09 | @ | ATL | W | 23 | 6 | Aaron Gordon | 1 | 6 | 16.7% | 0 | 4 | 1 | 2 | 4 | 5 | 0 | 2 | 2 | 6 | 0 | 1 | 3 | 1 | 17 | 6 |
| 223 | 2024-12-09 | @ | ATL | W | 17 | 5 | Jalen Pickett | 2 | 2 | 100.0% | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 2 | 5 | 1 | 0 | 2 | 4 | 18 | 11 |
| 224 | 2024-12-09 | @ | ATL | W | 8 | 5 | Hunter Tyson | 2 | 2 | 100.0% | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 7 | 7 |
| 225 | 2024-12-09 | @ | ATL | W | 10 | 4 | DeAndre Jordan | 2 | 2 | 100.0% | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 4 | 4 | 2 | 0 | 0 | 1 | 0 | 0 | 9 |
| 226 | 2024-12-09 | @ | ATL | W | 18 | 2 | Peyton Watson | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | 1 | 2 | 0 | 3 | 9 | 4 |
| 227 | 2024-12-09 | @ | ATL | W | 3 | 2 | P.J. Hall | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 5 | 2 |
| 228 | 2024-12-09 | @ | ATL | W | 3 | 2 | Trey Alexander | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 1 |
| 229 | 2024-12-09 | @ | ATL | W | 3 | 2 | Zeke Nnaji | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 |
| 230 | 2024-12-14 | vs | LAC | W | 32 | 20 | Jamal Murray | 8 | 16 | 50.0% | 2 | 5 | 6 | 11 | 2 | 2 | 0 | 5 | 5 | 3 | 4 | 1 | 6 | 2 | 18 | 19 |
| 231 | 2024-12-14 | vs | LAC | W | 29 | 17 | Michael Porter | 5 | 11 | 45.5% | 2 | 7 | 3 | 4 | 5 | 6 | 1 | 4 | 5 | 4 | 1 | 0 | 1 | 3 | 15 | 19 |
| 232 | 2024-12-14 | vs | LAC | W | 30 | 16 | Nikola Jokic | 6 | 9 | 66.7% | 2 | 2 | 4 | 7 | 2 | 4 | 0 | 7 | 7 | 2 | 2 | 0 | 5 | 2 | 14 | 17 |
| 233 | 2024-12-14 | vs | LAC | W | 26 | 14 | Peyton Watson | 5 | 6 | 83.3% | 1 | 2 | 4 | 4 | 3 | 4 | 2 | 3 | 5 | 0 | 2 | 0 | 0 | 0 | 9 | 19 |
| 234 | 2024-12-14 | vs | LAC | W | 23 | 14 | Aaron Gordon | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 5 | 5 | 0 | 6 | 6 | 2 | 0 | 0 | 1 | 2 | 10 | 18 |
| 235 | 2024-12-14 | vs | LAC | W | 26 | 12 | Julian Strawther | 3 | 6 | 50.0% | 0 | 2 | 3 | 4 | 6 | 7 | 0 | 4 | 4 | 2 | 0 | 0 | 1 | 1 | 7 | 13 |
| 236 | 2024-12-14 | vs | LAC | W | 24 | 8 | Christian Braun | 4 | 8 | 50.0% | 0 | 4 | 4 | 4 | 0 | 0 | 1 | 2 | 3 | 1 | 2 | 0 | 0 | 2 | 8 | 10 |
| 237 | 2024-12-14 | vs | LAC | W | 5 | 5 | Hunter Tyson | 1 | 1 | 100.0% | 1 | 1 | 0 | 0 | 2 | 2 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 1 | 0 | 4 |
| 238 | 2024-12-14 | vs | LAC | W | 24 | 5 | Russell Westbrook | 2 | 9 | 22.2% | 0 | 3 | 2 | 6 | 1 | 2 | 0 | 1 | 1 | 5 | 3 | 0 | 4 | 2 | 21 | 2 |
| 239 | 2024-12-14 | vs | LAC | W | 14 | 4 | DeAndre Jordan | 2 | 2 | 100.0% | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 8 | 9 | 2 | 1 | 1 | 0 | 1 | 8 | 17 |
| 240 | 2024-12-14 | vs | LAC | W | 4 | 3 | Jalen Pickett | 1 | 1 | 100.0% | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
| 241 | 2024-12-14 | vs | LAC | W | 4 | 2 | Zeke Nnaji | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 |
| 242 | 2024-12-17 | @ | SAC | W | 38 | 28 | Jamal Murray | 11 | 26 | 42.3% | 4 | 8 | 7 | 18 | 2 | 2 | 0 | 2 | 2 | 6 | 0 | 1 | 1 | 2 | 10 | 21 |
| 243 | 2024-12-17 | @ | SAC | W | 31 | 24 | Aaron Gordon | 9 | 14 | 64.3% | 1 | 3 | 8 | 11 | 5 | 6 | 4 | 3 | 7 | 2 | 0 | 0 | 1 | 3 | 6 | 26 |
| 244 | 2024-12-17 | @ | SAC | W | 38 | 20 | Nikola Jokic | 8 | 19 | 42.1% | 1 | 6 | 7 | 13 | 3 | 4 | 2 | 12 | 14 | 13 | 2 | 1 | 5 | 0 | 4 | 33 |
| 245 | 2024-12-17 | @ | SAC | W | 35 | 18 | Russell Westbrook | 8 | 13 | 61.5% | 0 | 3 | 8 | 10 | 2 | 4 | 3 | 6 | 9 | 10 | 3 | 0 | 3 | 3 | 3 | 30 |
| 246 | 2024-12-17 | @ | SAC | W | 23 | 13 | Julian Strawther | 6 | 12 | 50.0% | 1 | 4 | 5 | 8 | 0 | 0 | 0 | 4 | 4 | 1 | 0 | 1 | 0 | 1 | -11 | 13 |
| 247 | 2024-12-17 | @ | SAC | W | 31 | 11 | Michael Porter | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 2 | 3 | 2 | 8 | 10 | 1 | 1 | 0 | 2 | 1 | 8 | 17 |
| 248 | 2024-12-17 | @ | SAC | W | 17 | 9 | Peyton Watson | 3 | 8 | 37.5% | 1 | 1 | 2 | 7 | 2 | 3 | 3 | 1 | 4 | 1 | 1 | 0 | 1 | 3 | -5 | 8 |
| 249 | 2024-12-17 | @ | SAC | W | 17 | 7 | Hunter Tyson | 2 | 2 | 100.0% | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 2 | 3 | 1 | 1 | 0 | 1 | 3 | -7 | 11 |
| 250 | 2024-12-17 | @ | SAC | W | 10 | 0 | DeAndre Jordan | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | 2 | 3 | 1 | 1 | 1 | 2 | -3 | 5 |
| 251 | 2024-12-20 | @ | POR | L | 37 | 34 | Nikola Jokic | 13 | 18 | 72.2% | 4 | 6 | 9 | 12 | 4 | 4 | 0 | 6 | 6 | 8 | 1 | 0 | 3 | 4 | -5 | 41 |
| 252 | 2024-12-20 | @ | POR | L | 38 | 24 | Jamal Murray | 9 | 19 | 47.4% | 3 | 6 | 6 | 13 | 3 | 4 | 0 | 3 | 3 | 10 | 3 | 0 | 1 | 3 | 1 | 28 |
| 253 | 2024-12-20 | @ | POR | L | 33 | 19 | Russell Westbrook | 8 | 12 | 66.7% | 2 | 4 | 6 | 8 | 1 | 2 | 2 | 2 | 4 | 7 | 1 | 2 | 3 | 3 | 7 | 25 |
| 254 | 2024-12-20 | @ | POR | L | 32 | 16 | Michael Porter | 6 | 11 | 54.5% | 3 | 7 | 3 | 4 | 1 | 2 | 1 | 5 | 6 | 3 | 1 | 0 | 1 | 1 | 7 | 19 |
| 255 | 2024-12-20 | @ | POR | L | 25 | 13 | Christian Braun | 5 | 10 | 50.0% | 1 | 2 | 4 | 8 | 2 | 2 | 0 | 4 | 4 | 1 | 1 | 0 | 0 | 1 | -12 | 14 |
| 256 | 2024-12-20 | @ | POR | L | 17 | 8 | Peyton Watson | 2 | 3 | 66.7% | 0 | 1 | 2 | 2 | 4 | 5 | 1 | 1 | 2 | 1 | 0 | 0 | 1 | 2 | -2 | 8 |
| 257 | 2024-12-20 | @ | POR | L | 10 | 6 | Julian Strawther | 2 | 4 | 50.0% | 2 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2 | -17 | 5 |
| 258 | 2024-12-20 | @ | POR | L | 11 | 2 | DeAndre Jordan | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 2 | 5 | 7 | 0 | 0 | 1 | 0 | 2 | 3 | 9 |
| 259 | 2024-12-20 | @ | POR | L | 31 | 2 | Aaron Gordon | 1 | 6 | 16.7% | 0 | 3 | 1 | 3 | 0 | 2 | 1 | 5 | 6 | 3 | 0 | 0 | 2 | 2 | 0 | 2 |
| 260 | 2024-12-20 | @ | POR | L | 6 | 0 | Hunter Tyson | 0 | 2 | 0.0% | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 8 | 1 |
| 261 | 2024-12-23 | @ | NOP | W | 43 | 27 | Nikola Jokic | 11 | 20 | 55.0% | 2 | 3 | 9 | 17 | 3 | 5 | 2 | 11 | 13 | 10 | 1 | 1 | 3 | 5 | 6 | 38 |
| 262 | 2024-12-23 | @ | NOP | W | 41 | 27 | Jamal Murray | 9 | 19 | 47.4% | 2 | 9 | 7 | 10 | 7 | 8 | 1 | 7 | 8 | 4 | 3 | 1 | 6 | 2 | -5 | 26 |
| 263 | 2024-12-23 | @ | NOP | W | 36 | 21 | Russell Westbrook | 9 | 14 | 64.3% | 1 | 4 | 8 | 10 | 2 | 4 | 3 | 2 | 5 | 5 | 3 | 1 | 5 | 3 | 6 | 23 |
| 264 | 2024-12-23 | @ | NOP | W | 35 | 17 | Aaron Gordon | 6 | 12 | 50.0% | 2 | 4 | 4 | 8 | 3 | 6 | 5 | 3 | 8 | 3 | 1 | 0 | 2 | 2 | -6 | 18 |
| 265 | 2024-12-23 | @ | NOP | W | 20 | 13 | Julian Strawther | 4 | 8 | 50.0% | 1 | 5 | 3 | 3 | 4 | 4 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 2 | 20 | 12 |
| 266 | 2024-12-23 | @ | NOP | W | 39 | 10 | Christian Braun | 4 | 8 | 50.0% | 0 | 1 | 4 | 7 | 2 | 2 | 3 | 3 | 6 | 5 | 2 | 0 | 1 | 1 | 2 | 18 |
| 267 | 2024-12-23 | @ | NOP | W | 23 | 8 | Michael Porter | 2 | 8 | 25.0% | 2 | 6 | 0 | 2 | 2 | 2 | 0 | 4 | 4 | 1 | 0 | 2 | 4 | 2 | -10 | 5 |
| 268 | 2024-12-23 | @ | NOP | W | 18 | 5 | Peyton Watson | 2 | 5 | 40.0% | 0 | 1 | 2 | 4 | 1 | 1 | 0 | 5 | 5 | 5 | 0 | 2 | 1 | 1 | 5 | 13 |
| 269 | 2024-12-23 | @ | NOP | W | 10 | 4 | DeAndre Jordan | 2 | 3 | 66.7% | 0 | 0 | 2 | 3 | 0 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 0 | 1 | -3 | 7 |
| 270 | 2024-12-24 | vs | SUNS | W | 30 | 32 | Nikola Jokic | 12 | 17 | 70.6% | 4 | 6 | 8 | 11 | 4 | 5 | 0 | 2 | 2 | 7 | 0 | 0 | 1 | 1 | 19 | 34 |
| 271 | 2024-12-24 | vs | SUNS | W | 30 | 24 | Michael Porter | 10 | 12 | 83.3% | 2 | 4 | 8 | 8 | 2 | 2 | 2 | 4 | 6 | 4 | 0 | 0 | 1 | 1 | 22 | 31 |
| 272 | 2024-12-24 | vs | SUNS | W | 23 | 12 | Aaron Gordon | 4 | 7 | 57.1% | 2 | 3 | 2 | 4 | 2 | 2 | 0 | 2 | 2 | 1 | 1 | 0 | 0 | 3 | 9 | 13 |
| 273 | 2024-12-24 | vs | SUNS | W | 25 | 11 | Russell Westbrook | 5 | 12 | 41.7% | 1 | 5 | 4 | 7 | 0 | 0 | 3 | 2 | 5 | 7 | 2 | 0 | 3 | 2 | 10 | 15 |
| 274 | 2024-12-24 | vs | SUNS | W | 23 | 11 | Jalen Pickett | 4 | 11 | 36.4% | 3 | 8 | 1 | 3 | 0 | 0 | 0 | 3 | 3 | 8 | 0 | 1 | 2 | 0 | 17 | 14 |
| 275 | 2024-12-24 | vs | SUNS | W | 18 | 9 | Julian Strawther | 3 | 9 | 33.3% | 3 | 7 | 0 | 2 | 0 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 0 | 1 | 7 | 7 |
| 276 | 2024-12-24 | vs | SUNS | W | 16 | 6 | Hunter Tyson | 2 | 4 | 50.0% | 1 | 2 | 1 | 2 | 1 | 2 | 0 | 4 | 4 | 0 | 1 | 0 | 0 | 2 | 3 | 8 |
| 277 | 2024-12-24 | vs | SUNS | W | 14 | 4 | DeAndre Jordan | 2 | 2 | 100.0% | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 4 | 5 | 1 | 1 | 1 | 1 | 1 | 8 | 11 |
| 278 | 2024-12-24 | vs | SUNS | W | 26 | 4 | Christian Braun | 2 | 9 | 22.2% | 0 | 3 | 2 | 6 | 0 | 0 | 1 | 6 | 7 | 4 | 0 | 0 | 1 | 0 | 18 | 7 |
| 279 | 2024-12-24 | vs | SUNS | W | 21 | 4 | Peyton Watson | 2 | 5 | 40.0% | 0 | 1 | 2 | 4 | 0 | 0 | 3 | 3 | 6 | 1 | 0 | 1 | 2 | 2 | 14 | 7 |
| 280 | 2024-12-24 | vs | SUNS | W | 3 | 0 | P.J. Hall | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 1 | 0 | 0 | 2 | 3 |
| 281 | 2024-12-24 | vs | SUNS | W | 4 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 1 |
| 282 | 2024-12-24 | vs | SUNS | W | 4 | 0 | Spencer Jones | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 1 | 1 | 2 | 0 |
| 283 | 2024-12-24 | vs | SUNS | W | 4 | 0 | Zeke Nnaji | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | -1 |
| 284 | 2024-12-26 | @ | SUNS | L | 38 | 25 | Nikola Jokic | 10 | 19 | 52.6% | 2 | 7 | 8 | 12 | 3 | 4 | 5 | 10 | 15 | 2 | 2 | 0 | 2 | 1 | -3 | 32 |
| 285 | 2024-12-26 | @ | SUNS | L | 37 | 22 | Michael Porter | 7 | 14 | 50.0% | 3 | 8 | 4 | 6 | 5 | 7 | 2 | 2 | 4 | 2 | 0 | 1 | 5 | 2 | -4 | 15 |
| 286 | 2024-12-26 | @ | SUNS | L | 27 | 17 | Russell Westbrook | 6 | 12 | 50.0% | 1 | 4 | 5 | 8 | 4 | 7 | 1 | 5 | 6 | 2 | 1 | 0 | 4 | 1 | 3 | 13 |
| 287 | 2024-12-26 | @ | SUNS | L | 39 | 13 | Jamal Murray | 4 | 10 | 40.0% | 1 | 2 | 3 | 8 | 4 | 4 | 0 | 6 | 6 | 6 | 0 | 0 | 4 | 1 | -16 | 15 |
| 288 | 2024-12-26 | @ | SUNS | L | 19 | 7 | Aaron Gordon | 3 | 7 | 42.9% | 0 | 1 | 3 | 6 | 1 | 2 | 1 | 2 | 3 | 2 | 0 | 0 | 0 | 2 | -1 | 7 |
| 289 | 2024-12-26 | @ | SUNS | L | 28 | 5 | Christian Braun | 2 | 7 | 28.6% | 0 | 2 | 2 | 5 | 1 | 2 | 3 | 2 | 5 | 3 | 0 | 0 | 1 | 3 | -10 | 6 |
| 290 | 2024-12-26 | @ | SUNS | L | 18 | 5 | Julian Strawther | 2 | 4 | 50.0% | 1 | 2 | 1 | 2 | 0 | 0 | 1 | 1 | 2 | 1 | 0 | 0 | 0 | 3 | -8 | 6 |
| 291 | 2024-12-26 | @ | SUNS | L | 10 | 4 | DeAndre Jordan | 2 | 2 | 100.0% | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 1 | -7 | 6 |
| 292 | 2024-12-26 | @ | SUNS | L | 24 | 2 | Peyton Watson | 0 | 3 | 0.0% | 0 | 3 | 0 | 0 | 2 | 2 | 1 | 2 | 3 | 2 | 0 | 1 | 0 | 2 | -4 | 5 |
| 293 | 2024-12-28 | vs | CLE | L | 36 | 27 | Nikola Jokic | 12 | 19 | 63.2% | 0 | 3 | 12 | 16 | 3 | 4 | 3 | 11 | 14 | 13 | 3 | 0 | 1 | 1 | -15 | 48 |
| 294 | 2024-12-28 | vs | CLE | L | 40 | 27 | Jamal Murray | 10 | 20 | 50.0% | 3 | 6 | 7 | 14 | 4 | 4 | 0 | 3 | 3 | 11 | 2 | 0 | 4 | 3 | -9 | 29 |
| 295 | 2024-12-28 | vs | CLE | L | 26 | 18 | Peyton Watson | 6 | 8 | 75.0% | 2 | 2 | 4 | 6 | 4 | 5 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 0 | -16 | 20 |
| 296 | 2024-12-28 | vs | CLE | L | 32 | 18 | Michael Porter | 6 | 11 | 54.5% | 5 | 8 | 1 | 3 | 1 | 2 | 1 | 3 | 4 | 3 | 0 | 0 | 1 | 5 | -5 | 18 |
| 297 | 2024-12-28 | vs | CLE | L | 32 | 16 | Christian Braun | 7 | 9 | 77.8% | 2 | 4 | 5 | 5 | 0 | 0 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 4 | -21 | 17 |
| 298 | 2024-12-28 | vs | CLE | L | 30 | 11 | Russell Westbrook | 5 | 13 | 38.5% | 1 | 5 | 4 | 8 | 0 | 0 | 0 | 4 | 4 | 7 | 1 | 0 | 4 | 3 | -13 | 11 |
| 299 | 2024-12-28 | vs | CLE | L | 23 | 11 | Julian Strawther | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 2 | 2 | 0 | 2 | 2 | 0 | 0 | 0 | 2 | 0 | 6 | 8 |
| 300 | 2024-12-28 | vs | CLE | L | 11 | 4 | DeAndre Jordan | 2 | 4 | 50.0% | 0 | 0 | 2 | 4 | 0 | 1 | 3 | 4 | 7 | 1 | 0 | 2 | 0 | 3 | -1 | 11 |
| 301 | 2024-12-28 | vs | CLE | L | 6 | 3 | Hunter Tyson | 1 | 3 | 33.3% | 1 | 1 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | -2 | 2 |
| 302 | 2024-12-28 | vs | CLE | L | 2 | 0 | Jalen Pickett | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 1 |
| 303 | 2024-12-28 | vs | CLE | L | 2 | 0 | Zeke Nnaji | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 304 | 2024-12-28 | vs | CLE | L | 2 | 0 | Spencer Jones | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 |
| 305 | 2024-12-29 | vs | DET | W | 37 | 37 | Nikola Jokic | 11 | 17 | 64.7% | 4 | 5 | 7 | 12 | 11 | 14 | 1 | 8 | 9 | 8 | 0 | 0 | 2 | 4 | 25 | 43 |
| 306 | 2024-12-29 | vs | DET | W | 40 | 34 | Jamal Murray | 12 | 21 | 57.1% | 4 | 7 | 8 | 14 | 6 | 7 | 1 | 4 | 5 | 4 | 1 | 3 | 1 | 1 | 24 | 36 |
| 307 | 2024-12-29 | vs | DET | W | 34 | 26 | Michael Porter | 9 | 14 | 64.3% | 5 | 7 | 4 | 7 | 3 | 4 | 1 | 2 | 3 | 0 | 0 | 0 | 1 | 1 | 5 | 22 |
| 308 | 2024-12-29 | vs | DET | W | 30 | 10 | Christian Braun | 2 | 4 | 50.0% | 0 | 1 | 2 | 3 | 6 | 6 | 2 | 2 | 4 | 3 | 3 | 1 | 0 | 3 | 13 | 19 |
| 309 | 2024-12-29 | vs | DET | W | 31 | 8 | Russell Westbrook | 4 | 6 | 66.7% | 0 | 1 | 4 | 5 | 0 | 0 | 2 | 7 | 9 | 8 | 2 | 0 | 6 | 4 | 13 | 19 |
| 310 | 2024-12-29 | vs | DET | W | 11 | 6 | DeAndre Jordan | 2 | 2 | 100.0% | 0 | 0 | 2 | 2 | 2 | 4 | 0 | 3 | 3 | 0 | 0 | 1 | 1 | 0 | -12 | 7 |
| 311 | 2024-12-29 | vs | DET | W | 23 | 6 | Julian Strawther | 3 | 9 | 33.3% | 0 | 4 | 3 | 5 | 0 | 0 | 0 | 3 | 3 | 1 | 1 | 0 | 1 | 3 | -5 | 4 |
| 312 | 2024-12-29 | vs | DET | W | 23 | 5 | Peyton Watson | 2 | 6 | 33.3% | 0 | 1 | 2 | 5 | 1 | 1 | 0 | 1 | 1 | 3 | 2 | 1 | 0 | 2 | -6 | 8 |
| 313 | 2024-12-29 | vs | DET | W | 13 | 2 | Hunter Tyson | 1 | 3 | 33.3% | 0 | 1 | 1 | 2 | 0 | 0 | 1 | 2 | 3 | 2 | 0 | 0 | 1 | 0 | 8 | 4 |
| 314 | 2024-12-31 | @ | UTAH | W | 38 | 36 | Nikola Jokic | 14 | 33 | 42.4% | 3 | 9 | 11 | 24 | 5 | 6 | 7 | 15 | 22 | 11 | 4 | 0 | 2 | 0 | 20 | 51 |
| 315 | 2024-12-31 | @ | UTAH | W | 35 | 21 | Michael Porter | 8 | 18 | 44.4% | 3 | 6 | 5 | 12 | 2 | 3 | 5 | 1 | 6 | 2 | 0 | 0 | 0 | 4 | 13 | 18 |
| 316 | 2024-12-31 | @ | UTAH | W | 37 | 20 | Jamal Murray | 7 | 17 | 41.2% | 2 | 6 | 5 | 11 | 4 | 4 | 1 | 3 | 4 | 10 | 2 | 1 | 3 | 2 | -9 | 24 |
| 317 | 2024-12-31 | @ | UTAH | W | 36 | 20 | Christian Braun | 9 | 12 | 75.0% | 0 | 1 | 9 | 11 | 2 | 2 | 0 | 2 | 2 | 2 | 2 | 1 | 0 | 3 | 20 | 24 |
| 318 | 2024-12-31 | @ | UTAH | W | 33 | 16 | Russell Westbrook | 7 | 7 | 100.0% | 0 | 0 | 7 | 7 | 2 | 2 | 3 | 7 | 10 | 10 | 4 | 0 | 0 | 3 | 23 | 40 |
| 319 | 2024-12-31 | @ | UTAH | W | 26 | 13 | Peyton Watson | 6 | 9 | 66.7% | 1 | 3 | 5 | 6 | 0 | 0 | 0 | 4 | 4 | 1 | 0 | 1 | 1 | 1 | -2 | 15 |
| 320 | 2024-12-31 | @ | UTAH | W | 18 | 4 | Julian Strawther | 1 | 5 | 20.0% | 0 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 0 | 1 | 2 | 5 | 3 |
| 321 | 2024-12-31 | @ | UTAH | W | 10 | 2 | DeAndre Jordan | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | -9 | 3 |
| 322 | 2024-12-31 | @ | UTAH | W | 7 | 0 | Hunter Tyson | 0 | 2 | 0.0% | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -6 | -2 |
| 323 | 2025-01-02 | vs | ATL | W | 30 | 23 | Nikola Jokic | 8 | 16 | 50.0% | 1 | 1 | 7 | 15 | 6 | 6 | 3 | 14 | 17 | 15 | 0 | 1 | 2 | 2 | 31 | 46 |
| 324 | 2025-01-02 | vs | ATL | W | 24 | 21 | Michael Porter | 8 | 14 | 57.1% | 5 | 9 | 3 | 5 | 0 | 0 | 0 | 4 | 4 | 2 | 1 | 0 | 1 | 1 | 24 | 21 |
| 325 | 2025-01-02 | vs | ATL | W | 36 | 21 | Jamal Murray | 6 | 14 | 42.9% | 2 | 5 | 4 | 9 | 7 | 7 | 1 | 2 | 3 | 2 | 2 | 1 | 2 | 1 | 11 | 19 |
| 326 | 2025-01-02 | vs | ATL | W | 26 | 16 | Russell Westbrook | 5 | 6 | 83.3% | 1 | 2 | 4 | 4 | 5 | 5 | 0 | 2 | 2 | 11 | 0 | 0 | 2 | 3 | 24 | 26 |
| 327 | 2025-01-02 | vs | ATL | W | 32 | 15 | Christian Braun | 7 | 9 | 77.8% | 0 | 1 | 7 | 8 | 1 | 1 | 0 | 3 | 3 | 4 | 0 | 0 | 0 | 3 | 23 | 20 |
| 328 | 2025-01-02 | vs | ATL | W | 30 | 13 | Julian Strawther | 6 | 12 | 50.0% | 1 | 5 | 5 | 7 | 0 | 0 | 0 | 6 | 6 | 2 | 2 | 1 | 1 | 2 | 4 | 17 |
| 329 | 2025-01-02 | vs | ATL | W | 24 | 11 | Peyton Watson | 4 | 10 | 40.0% | 1 | 4 | 3 | 6 | 2 | 2 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 2 | 3 | 7 |
| 330 | 2025-01-02 | vs | ATL | W | 15 | 8 | DeAndre Jordan | 4 | 4 | 100.0% | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 7 | 7 | 3 | 1 | 1 | 1 | 1 | -6 | 19 |
| 331 | 2025-01-02 | vs | ATL | W | 13 | 7 | Jalen Pickett | 3 | 5 | 60.0% | 1 | 2 | 2 | 3 | 0 | 0 | 0 | 1 | 1 | 4 | 0 | 0 | 1 | 1 | -4 | 9 |
| 332 | 2025-01-02 | vs | ATL | W | 3 | 2 | Zeke Nnaji | 1 | 2 | 50.0% | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -6 | 1 |
| 333 | 2025-01-02 | vs | ATL | W | 3 | 2 | Spencer Jones | 1 | 2 | 50.0% | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -6 | 1 |
| 334 | 2025-01-02 | vs | ATL | W | 4 | 0 | Hunter Tyson | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -3 | 0 |
| 335 | 2025-01-04 | vs | SAS | L | 37 | 41 | Nikola Jokic | 15 | 36 | 41.7% | 3 | 10 | 12 | 26 | 8 | 9 | 4 | 14 | 18 | 9 | 2 | 0 | 2 | 3 | 7 | 46 |
| 336 | 2025-01-04 | vs | SAS | L | 42 | 22 | Michael Porter | 9 | 14 | 64.3% | 4 | 8 | 5 | 6 | 0 | 0 | 1 | 3 | 4 | 1 | 0 | 2 | 1 | 0 | 7 | 23 |
| 337 | 2025-01-04 | vs | SAS | L | 37 | 14 | Jamal Murray | 6 | 17 | 35.3% | 2 | 5 | 4 | 12 | 0 | 0 | 1 | 3 | 4 | 7 | 0 | 0 | 2 | 1 | -7 | 12 |
| 338 | 2025-01-04 | vs | SAS | L | 43 | 11 | Christian Braun | 3 | 10 | 30.0% | 2 | 6 | 1 | 4 | 3 | 4 | 1 | 7 | 8 | 5 | 0 | 2 | 0 | 2 | 0 | 18 |
| 339 | 2025-01-04 | vs | SAS | L | 23 | 11 | Julian Strawther | 4 | 7 | 57.1% | 3 | 6 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 2 | 1 | 11 |
| 340 | 2025-01-04 | vs | SAS | L | 31 | 9 | Russell Westbrook | 4 | 7 | 57.1% | 1 | 2 | 3 | 5 | 0 | 2 | 1 | 5 | 6 | 8 | 2 | 0 | 3 | 5 | 6 | 17 |
| 341 | 2025-01-04 | vs | SAS | L | 19 | 2 | Peyton Watson | 1 | 4 | 25.0% | 0 | 1 | 1 | 3 | 0 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 0 | 0 | -10 | 6 |
| 342 | 2025-01-04 | vs | SAS | L | 4 | 0 | DeAndre Jordan | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | -8 | 1 |
| 343 | 2025-01-04 | vs | SAS | L | 5 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | -11 | 1 |
| 344 | 2025-01-05 | @ | SAS | W | 43 | 46 | Nikola Jokic | 19 | 35 | 54.3% | 3 | 8 | 16 | 27 | 5 | 6 | 4 | 5 | 9 | 10 | 2 | 2 | 2 | 3 | 7 | 50 |
| 345 | 2025-01-05 | @ | SAS | W | 46 | 28 | Michael Porter | 9 | 17 | 52.9% | 4 | 9 | 5 | 8 | 6 | 7 | 2 | 8 | 10 | 2 | 0 | 0 | 1 | 1 | 21 | 30 |
| 346 | 2025-01-05 | @ | SAS | W | 38 | 13 | Jamal Murray | 6 | 17 | 35.3% | 1 | 3 | 5 | 14 | 0 | 0 | 0 | 6 | 6 | 6 | 5 | 1 | 1 | 3 | 4 | 19 |
| 347 | 2025-01-05 | @ | SAS | W | 36 | 9 | Russell Westbrook | 4 | 11 | 36.4% | 1 | 4 | 3 | 7 | 0 | 0 | 4 | 6 | 10 | 6 | 3 | 1 | 1 | 3 | 5 | 21 |
| 348 | 2025-01-05 | @ | SAS | W | 34 | 8 | Peyton Watson | 2 | 6 | 33.3% | 0 | 3 | 2 | 3 | 4 | 6 | 1 | 7 | 8 | 3 | 0 | 2 | 1 | 0 | 21 | 14 |
| 349 | 2025-01-05 | @ | SAS | W | 27 | 8 | Christian Braun | 4 | 8 | 50.0% | 0 | 1 | 4 | 7 | 0 | 0 | 0 | 2 | 2 | 3 | 1 | 1 | 0 | 2 | -15 | 11 |
| 350 | 2025-01-05 | @ | SAS | W | 26 | 5 | Julian Strawther | 1 | 9 | 11.1% | 1 | 5 | 0 | 4 | 2 | 2 | 0 | 5 | 5 | 2 | 0 | 0 | 0 | 2 | 7 | 4 |
| 351 | 2025-01-05 | @ | SAS | W | 10 | 3 | DeAndre Jordan | 1 | 4 | 25.0% | 0 | 0 | 1 | 4 | 1 | 2 | 3 | 3 | 6 | 0 | 0 | 0 | 1 | 2 | 4 | 4 |
| 352 | 2025-01-05 | @ | SAS | W | 4 | 2 | Jalen Pickett | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 4 | 4 |
| 353 | 2025-01-05 | @ | SAS | W | 1 | 0 | Hunter Tyson | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | -3 | 0 |
| 354 | 2025-01-08 | vs | BOS | L | 33 | 26 | Russell Westbrook | 9 | 18 | 50.0% | 4 | 9 | 5 | 9 | 4 | 5 | 3 | 6 | 9 | 6 | 1 | 1 | 8 | 2 | -10 | 25 |
| 355 | 2025-01-08 | vs | BOS | L | 39 | 19 | Jamal Murray | 8 | 17 | 47.1% | 1 | 5 | 7 | 12 | 2 | 2 | 2 | 2 | 4 | 4 | 2 | 0 | 1 | 3 | -3 | 19 |
| 356 | 2025-01-08 | vs | BOS | L | 27 | 19 | Julian Strawther | 8 | 15 | 53.3% | 3 | 6 | 5 | 9 | 0 | 0 | 1 | 1 | 2 | 2 | 1 | 0 | 1 | 2 | -3 | 16 |
| 357 | 2025-01-08 | vs | BOS | L | 36 | 15 | Michael Porter | 5 | 13 | 38.5% | 2 | 7 | 3 | 6 | 3 | 4 | 1 | 9 | 10 | 3 | 0 | 0 | 1 | 1 | -16 | 18 |
| 358 | 2025-01-08 | vs | BOS | L | 34 | 14 | Peyton Watson | 5 | 9 | 55.6% | 2 | 3 | 3 | 6 | 2 | 2 | 2 | 2 | 4 | 0 | 1 | 4 | 1 | 3 | -3 | 18 |
| 359 | 2025-01-08 | vs | BOS | L | 29 | 12 | Christian Braun | 5 | 10 | 50.0% | 2 | 5 | 3 | 5 | 0 | 0 | 2 | 2 | 4 | 2 | 1 | 0 | 2 | 3 | -11 | 12 |
| 360 | 2025-01-08 | vs | BOS | L | 20 | 1 | DeAndre Jordan | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 3 | 5 | 1 | 0 | 0 | 1 | 2 | 1 | 4 |
| 361 | 2025-01-08 | vs | BOS | L | 17 | 0 | Dario Saric | 0 | 2 | 0.0% | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 4 | 0 | 1 | 1 | 1 | -7 | 3 |
| 362 | 2025-01-08 | vs | BOS | L | 1 | 0 | Hunter Tyson | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 363 | 2025-01-08 | vs | BOS | L | 1 | 0 | Trey Alexander | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 364 | 2025-01-08 | vs | BOS | L | 1 | 0 | Jalen Pickett | 0 | 0 | - | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 365 | 2025-01-08 | vs | BOS | L | 5 | 0 | Zeke Nnaji | 0 | 2 | 0.0% | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -8 | -2 |
| 366 | 2025-01-09 | vs | LAC | W | 34 | 21 | Jamal Murray | 7 | 13 | 53.8% | 4 | 6 | 3 | 7 | 3 | 3 | 0 | 3 | 3 | 9 | 1 | 1 | 2 | 1 | 24 | 27 |
| 367 | 2025-01-09 | vs | LAC | W | 27 | 19 | Michael Porter | 8 | 12 | 66.7% | 3 | 5 | 5 | 7 | 0 | 0 | 0 | 8 | 8 | 2 | 1 | 0 | 2 | 1 | 15 | 24 |
| 368 | 2025-01-09 | vs | LAC | W | 29 | 19 | Russell Westbrook | 8 | 16 | 50.0% | 1 | 5 | 7 | 11 | 2 | 5 | 2 | 4 | 6 | 8 | 0 | 0 | 3 | 3 | 18 | 19 |
| 369 | 2025-01-09 | vs | LAC | W | 32 | 16 | Julian Strawther | 5 | 10 | 50.0% | 4 | 8 | 1 | 2 | 2 | 2 | 0 | 4 | 4 | 2 | 0 | 0 | 1 | 3 | 18 | 16 |
| 370 | 2025-01-09 | vs | LAC | W | 28 | 15 | Christian Braun | 6 | 8 | 75.0% | 2 | 2 | 4 | 6 | 1 | 1 | 0 | 2 | 2 | 1 | 1 | 0 | 0 | 4 | 11 | 17 |
| 371 | 2025-01-09 | vs | LAC | W | 23 | 12 | DeAndre Jordan | 5 | 6 | 83.3% | 0 | 0 | 5 | 6 | 2 | 3 | 2 | 7 | 9 | 2 | 2 | 0 | 0 | 2 | 17 | 23 |
| 372 | 2025-01-09 | vs | LAC | W | 24 | 9 | Peyton Watson | 4 | 10 | 40.0% | 0 | 2 | 4 | 8 | 1 | 3 | 0 | 4 | 4 | 1 | 1 | 2 | 1 | 3 | 17 | 8 |
| 373 | 2025-01-09 | vs | LAC | W | 20 | 7 | Dario Saric | 3 | 8 | 37.5% | 1 | 4 | 2 | 4 | 0 | 0 | 3 | 4 | 7 | 2 | 1 | 0 | 2 | 2 | 11 | 10 |
| 374 | 2025-01-09 | vs | LAC | W | 4 | 6 | Jalen Pickett | 2 | 2 | 100.0% | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 6 |
| 375 | 2025-01-09 | vs | LAC | W | 4 | 2 | Zeke Nnaji | 1 | 1 | 100.0% | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | -5 | 2 |
| 376 | 2025-01-09 | vs | LAC | W | 4 | 0 | Trey Alexander | 0 | 2 | 0.0% | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | -5 | 1 |
| 377 | 2025-01-09 | vs | LAC | W | 9 | 0 | Hunter Tyson | 0 | 1 | 0.0% | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 3 | -1 | 0 |
nugget_regular_season_df.groupby(['PLAYER'])['Pts'].mean()
PLAYER Aaron Gordon 13.705882 Christian Braun 13.857143 Dario Saric 3.416667 DeAndre Jordan 3.333333 Hunter Tyson 2.269231 Jalen Pickett 3.090909 Jamal Murray 19.433333 Julian Strawther 9.472222 Michael Porter 19.027778 Nikola Jokic 31.516129 P.J. Hall 0.666667 Peyton Watson 8.805556 Russell Westbrook 12.611111 Spencer Jones 0.400000 Trey Alexander 0.750000 Vlatko Cancar 2.250000 Zeke Nnaji 1.190476 Name: Pts, dtype: float64
#nugget_regular_season_df = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail().reset_index(drop=True)
nugget_regular_season_df["vs_opp_win_loss"] = nugget_regular_season_df['W/L']+' '+nugget_regular_season_df["Hm/Aw"]+' '+ nugget_regular_season_df["Opp"]
nugget_regular_season_df["GM_DAY"] = nugget_regular_season_df["Date"]+' '+nugget_regular_season_df["vs_opp_win_loss"]
def perplayer_performance():
players_df = nugget_regular_season_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
'TOV', 'FGA', 'FGM',
'3PA', '3PM','2PM','2PA',"OREB","DREB",
'FTA', 'FTM', 'PF','+/-','EFF']].sum().astype(int).sort_values(by='Pts',ascending=False)
style.use('ggplot')
plt.figure(figsize=(30, 18))
plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=18, labelbottom=True,
bottom=False, top=False, labeltop=True)
sns.heatmap(players_df, annot=True, fmt="d", cmap='viridis_r', annot_kws={"size":22}).set_title(f"Players Performance Data Scorecard Overview ", fontdict={'size': 30}, pad=30, color='#000')
perplayer_performance()
This chart above is a Players Performance Scoreboard Overview for denver nuggets players in this season. It includes various statistics for players like Nikola Jokic, Michael Porter Jr., Jamal Murray, and others. The columns represent total different performance metrics such as minutes played (MIN), total (PTS), rebounds (REB), assists (AST), turnovers (TOV), and shooting statistics like field goals attempted (FGA) and made (FGM), three-pointers attempted (3PA) and made (3PM), steals (STL), blocks (BLK), turnovers (TOV), and efficiency rating (EFF).
The table uses a color gradient to indicate performance levels, with deeper color representing high performance and light color indicating lower performance. This visual representation helps quickly identify how each player is performing in different areas of the game.
Below are some ot the top contributors based on the efficiency (EFF) scores from the scoreboard:
Nikola Jokic: With an EFF score of 1314, Jokic is a standout performer. Known for his exceptional passing, scoring, and rebounding abilities, he is a key player for the Denver Nuggets and a two-time NBA MVP.
Michael Porter Jr.: Porter Jr. has an EFF score of 742. He is a versatile forward known for his scoring prowess and three-point shooting. His ability to stretch the floor makes him a valuable asset to the team.
Jamal Murray: Murray's EFF score is 610. He is a dynamic guard known for his scoring ability and clutch performances. His partnership with Jokic forms a formidable duo for the Nuggets.
Christian Braun: With an EFF score of 581, Braun is a promising young player. He contributes significantly in various aspects of the game, including scoring, rebounding, and defense.
Russell Westbrook: Westbrook has an EFF score of 600. Known for his explosive athleticism and triple-double capabilities, he brings energy and intensity to the court.
These players have consistently delivered high performances, contributing significantly to the team's success.
nugget_regular_season_df['Pts'].sum()
4338
def perplayer_performance():
players_df = nugget_regular_season_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
'TOV', 'FGA', 'FGM',
'3PA', '3PM','2PM','2PA',"OREB","DREB",
'FTA', 'FTM', 'PF','+/-','EFF']].mean().astype(int).sort_values(by='Pts',ascending=False)
style.use('ggplot')
plt.figure(figsize=(30, 18))
plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=18, labelbottom=True,
bottom=False, top=False, labeltop=True)
sns.heatmap(players_df, annot=True, fmt="d", cmap='viridis_r', annot_kws={"size":22}).set_title(f" Players Average Performaces Overview", fontdict={'size': 30}, pad=30, color='#000')
perplayer_performance()
This chart Players Average Performances Overview is show the averarge performances of the players in each metric of the season.
def performance_trend(players_game, player,cmaps):
players_df = players_game[players_game['PLAYER'].isin([player])]
players_df = players_df.groupby(["Date", "W/L", "Hm/Aw","Opp"])[['MIN','Pts','REB','AST', 'BLK',
'TOV', 'FGA', 'FGM',
'3PA', '3PM','2PM','2PA',"OREB","DREB",
'FTA', 'FTM', 'PF','+/-','EFF']].sum()#.astype(int)
style.use('ggplot')
plt.figure(figsize=(18, 15))
plt.ylabel("Players", fontsize=15, color='black', fontweight='bold')
plt.tick_params(axis='both', which='major', labelcolor="black", labelsize=13, labelbottom=True,
bottom=False, top=False, labeltop=True)
sns.heatmap(players_df, annot=True, fmt="d", cmap=cmaps, annot_kws={"size":15}).set_title(f"Performance Data Scorecard for {player}", fontdict={'size': 16}, pad=15, color='#000')
This snippets below is a function that takes some parameters to display the statitical summary of the players.
def statistical_summary(stat, player, player_name, cmap):
players_df = (nugget_regular_season_df[nugget_regular_season_df[stat].isin([player])]).describe()
players_df.drop(columns=['MIN' ], inplace=True)
# Removed the count row and changing the floating numbers to type int.
stat_heatmap = (players_df.loc[[ 'mean', 'std', 'min', '25%', '50%', '75%', 'max']]).astype(int)
style.use('ggplot')
plt.figure(figsize=(15, 5))
plt.tick_params(axis='both', which='major',labelcolor='black', labelsize=11,labelbottom=True, bottom=False, top=False, labeltop=True)
sns.heatmap(stat_heatmap, annot=True,fmt="d", cmap=cmap, annot_kws={"size": 14}).set_title(f"Statistical Summary For {player_name}", fontdict={'size': 16},pad=15, color='#000' );
The function below accepts several parameters to display the average statistics for each player in the current season.
def season_average(player, image_path, annotation_text, color, mycmap):
nugget_group_df = nugget_regular_season_df.groupby(["PLAYER"])[['Pts', 'FGA', 'FGM', '3PM', '3PA', 'FTM', 'FTA', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TOV', 'PF']].mean().reset_index()
player_mean_stats = nugget_group_df[nugget_group_df['PLAYER'] == player]
# Collect the relevant statistics
player_mean_stats = player_mean_stats[['TOV', 'STL', 'BLK', 'FTM', '3PM', 'FGM', 'AST', 'REB', 'Pts']]
# Convert the stats to numeric values (in case of any non-numeric values)
mean_stats = player_mean_stats.apply(pd.to_numeric, errors='coerce').squeeze()
my_cmap = mycmap#plt.get_cmap("Accent")
style.use('dark_background')
plt.rcParams.update({
'figure.facecolor': 'black',
'font.size': 10,
})
fig, ax = plt.subplots(figsize=(20, 10))
# Add the player's picture
pic = plt.imread(image_path)
imagebox = offsetbox.OffsetImage(pic, zoom=0.1)
ab = offsetbox.AnnotationBbox(imagebox, (0.7, 0.54), xycoords='axes fraction', frameon=False)
ax.add_artist(ab)
# Add annotation text with transparent background and adjusted box dimensions
plt.text(
0.42, 0.33, annotation_text, fontsize=16, color=color, wrap=True,
ha='left', va='center', transform=ax.transAxes,
bbox=dict(facecolor="none", edgecolor="orange", boxstyle="round,pad=1.3")
)
# Apply normalization to the numeric values
norm = plt.Normalize(mean_stats.min(), mean_stats.max())
colors = my_cmap(norm(mean_stats))
# Create the horizontal bar chart without borderlines
bars = ax.barh(mean_stats.index, mean_stats.values, color=colors, edgecolor='none')
# Add value labels above each bar
for bar in bars:
width = bar.get_width()
plt.text(width, bar.get_y() + bar.get_height() / 2, f"{width:.1f}", ha='left', va='center', color="white", fontweight='bold', fontsize=20)
# Remove the spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.spines['bottom'].set_visible(False)
plt.yticks(fontweight='bold', fontsize=20, color="yellowgreen")
plt.xticks(fontweight='bold', fontsize=20, color="yellowgreen")
plt.title(f"{player} - Current Season's Average", fontsize=30, fontweight='bold', pad=20)
plt.grid(False)
plt.show()
This function, players_total_stats, calculates the total statistics for a specified player from the Denver Nuggets' regular season data.
def players_total_stats(stat, player):
players_stats = nugget_regular_season_df[nugget_regular_season_df[stat].isin([player])]
total_stat = players_stats[['Pts','FGM','FGA','3PM','3PA','2PM', '2PA', 'FTM', 'FTA','OREB','DREB', 'AST', 'STL', 'BLK', 'TOV', 'EFF']].sum().astype(int)
total_stats = total_stat.reset_index()
total_stats.columns = ['statistics', 'values']
return total_stats
This function, overall_stats, generates a comprehensive visual summary of a player's performance statistics for the regular season.
def overall_stats(player, limit ):
# Define the fg_percent function to return data
def fg_percent(fg_made, fg_attempted):
made_percentage = (fg_made / fg_attempted) * 100
missed_percentage = 100 - made_percentage
labels = ['made', 'missed']
sizes = [made_percentage, missed_percentage]
colors = ['#15B01A', 'red']
return {'sizes': sizes, 'labels': labels, 'colors': colors}
# Creating a figure
fig = plt.figure(figsize=(20, 10),facecolor='cyan')
style.use('dark_background')
# Set the spacing between subplots
plt.subplots_adjust(hspace=25)
# Defining the grid layout
gs = gridspec.GridSpec(2, 4, width_ratios=[1, 1, 1,1])
# Creating subplots
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1, 1])
ax4 = fig.add_subplot(gs[1, 2])
ax5 = fig.add_subplot(gs[1, 3])
#######################################################################################
######################################################################################
my_cmap = plt.get_cmap("coolwarm")
#my_cmap = plt.get_cmap("twilight_shifted_r")
style.use('dark_background')
norm = plt.Normalize(total_stats['values'].min(),total_stats['values'].max())
colors = my_cmap(norm(total_stats['values']))
ax1.bar(total_stats['statistics'], total_stats['values'], color=colors, edgecolor='black')
# Adding value labels on top of each bar
for i, value in enumerate(total_stats['values']):
ax1.text(i, value, str(value), ha='center', va='bottom', fontsize=20, color='orange')
# Customize the plot
ax1.set_xlabel('Statistics',fontsize=30, fontweight='bold', color='black')
ax1.set_ylabel('Totals',fontsize=30, fontweight='bold', color='black')
ax1.set_title(f"Regular Season Overall Sum of Box Scores For {player}.",fontsize=30,fontweight='bold', color='black')
ax1.tick_params(axis='x', colors='black',labelsize=20);
ax1.set_ylim(0, limit)
ax1.grid(False)
###################################################
#######################################################################################
##########################################################################################
cmap = plt.get_cmap("Reds")
colors = cmap([0.1, 0.5, 0.9])
# Plot the pie chart for ax1
data1 = fg_percent(total_stats.iloc[1, 1], total_stats.iloc[2, 1])
ax2.pie(data1['sizes'], labels=data1['labels'],
colors=colors,
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'xx-large', 'color': 'black'},startangle=126)
ax2.set_title(' Total Field Goals%',fontweight='bold',fontsize=20,color='black')
ax2.axis('equal')
#######################################################################################
# Plot the pie chart for ax2
data2 = fg_percent(total_stats.iloc[3, 1], total_stats.iloc[4, 1])
ax3.pie(data2['sizes'], labels=data2['labels'],
colors=data2['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'xx-large', 'color': 'black'},startangle=270)
ax3.set_title(' Total 3 points%',fontweight='bold',fontsize=20,color='black')
ax3.axis('equal')
#######################################################################################
# Plot the pie chart for ax3
data3 = fg_percent(total_stats.iloc[5, 1], total_stats.iloc[6, 1])
ax4.pie(data3['sizes'], labels=data3['labels'],
colors=data3['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'xx-large', 'color': 'black'},startangle=15)
ax4.set_title('Total 2 Points%',fontweight='bold',fontsize=20,color='black')
ax4.axis('equal')
#######################################################################################
# Plot the pie chart for ax3
data4 = fg_percent(total_stats.iloc[7, 1], total_stats.iloc[8, 1])
ax5.pie(data4['sizes'], labels=data4['labels'],
colors=data4['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'xx-large', 'color': 'black'},startangle=15)
ax5.set_title('Total Free-Throws%',fontweight='bold',fontsize=20,color='black')
ax5.axis('equal')
#######################################################################################
fig.tight_layout(pad=3)
# Show the plot
plt.show()
The function tripple_double_double identifies and generates a list of players who achieved triple-double performances in any game played.
def tripple_double_double(player):
# Initialize the counter and list to store triple-double performances
triple_double_count = 0
double_double_count = 0
triple_double_list = []
triple_double_counts = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]
# Check for triple-double performances and count them
for index, row in triple_double_counts.iterrows():
pts, reb, ast, opp, date = row['Pts'], row['REB'], row['AST'], row['Opp'], row['GM_DAY']
if pts >= 10 and reb >= 10 and ast >= 10:
#print(f"Triple-double performance: {pts, reb, ast} against {opp} on {date}")
triple_double_count += 1
triple_double_list.append((date, opp, pts, reb, ast))
elif (pts >= 10 and reb >= 10 and ast < 10) or (pts >= 10 and reb < 10 and ast >= 10) or (pts < 10 and reb >= 10 and ast >= 10):
#print(f"Double-double performance: {pts, reb, ast} against {opp} on {date}")
double_double_count += 1
# Create a DataFrame to store the triple-double performances
triple_double_df = pd.DataFrame(triple_double_list, columns=['Date', 'Opp', 'Pts', 'Rebs', 'Ast'])
# Print the total count of triple-double and double-double performances
print(f"Total triple-double performances for {player}: {triple_double_count}")
print(f"Total double-double performances for {player}: {double_double_count}")
return triple_double_df
#tripple_double_double("Nikola Jokic")
This function, triple_double_overview, generates a heatmap to visualize the triple-double performances of Nikola Jokic during the 2024/2025 season, summarizing points, rebounds, and assists for each game date.
def triple_double_overview(player):
triple_double_count = triple_double_df.groupby(["Date",])[['Pts','Rebs','Ast']].sum().astype(int)
style.use('dark_background')
plt.figure(figsize=(3, 5))
plt.tick_params(axis='both', which='major', labelsize=11,labelcolor='white',labelbottom=True, bottom=False, top=False, labeltop=True)
sns.heatmap(triple_double_count, annot=True,fmt="d", cmap='viridis_r',annot_kws={"size": 14}, color='#000' , cbar=False).set_title(f" {player} 2024/2025 Triple Double Overview.", fontdict={'size': 16},pad=15, color='white' );
This shows each player's performance both in home and away games
def home_away_performances(player):
#Displaying the plots in a subplots
def subplots():
# Create a figure
fig = plt.figure(figsize=(20,10));
fig.set_facecolor("orange")
plt.rcParams.update({'font.size': 10, })
# Defining the grid layout with different width ratios for each row
gs = gridspec.GridSpec(2, 2)
# Create subplots
ax1 = fig.add_subplot(gs[0, :]);
ax2 = fig.add_subplot(gs[1, :]);
style.use('ggplot')
%matplotlib inline
#sns.set_palette("dark")
#style.use('ggplot')
#sns.set_style('darkgrid')
######################################################################################################
####################### FREQUENCY DISTRIBUTION OF PERFORMANCES IN HOME GAMES #########################
######################################################################################################
player_home_performance = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]
home_games = player_home_performance[player_home_performance['Hm/Aw'] == 'vs' ]
home_game = home_games.describe()
home_game = (home_game.loc[[ 'max', 'min']]).astype(int)
# Accessing the indexes and values of the away game data
stats = home_game.loc['max'].index
home_max_values = home_game.loc['max'].values
home_min_values = home_game.loc['min'].values
y = np.arange(len(stats)) # The label locations
width = 0.4 # The width of the bars
# Plot the bars for each attribute
bar3 = ax1.bar(y - width, home_max_values, width, label='Home max values', color='green')
bar4 = ax1.bar(y, home_min_values, width, label='Home min values')
# Add value labels on top of each bar3
for p in bar3:
height = p.get_height()
ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
ha='center', va='center', fontsize=15, color='black', fontweight='bold')
# Add value labels on top of each bar4
for p in bar4:
height = p.get_height()
ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
ha='center', va='center', fontsize=15, color='black', fontweight='bold')
# Add labels, title, and custom x-axis tick labels
#ax2.set_ylabel('Counts',fontsize=15,fontweight='bold')
ax1.set_title('Performance Comparison: Home Games', fontsize=20, fontweight='bold',color='black')
ax1.set_xticks(y-width/2)
ax1.tick_params(axis='x', labelsize=15, labelcolor='black') # Adjust the font size as needed
ax1.tick_params(axis='y', labelsize=15, labelcolor='black')
ax1.set_xticklabels(stats)
ax1.legend(loc='upper center', ncols=3,fontsize=15,labelcolor='black')
ax1.set_ylim(0, 70)
######################################################################################################
####################### FREQUENCY DISTRIBUTION OF PERFORMANCES IN AWAY GAMES #########################
######################################################################################################
player_home_performance = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin([player])]
away_games = player_home_performance[player_home_performance['Hm/Aw'] == '@' ]
away_game = away_games.describe()
away_game = (away_game.loc[[ 'max', 'min']]).astype(int)
# Accessing the indexes and values of the away game data
stats =away_game.loc['max'].index
away_max_values = away_game.loc['max'].values
away_min_values = away_game.loc['min'].values
y = np.arange(len(stats)) # The label locations
width = 0.4 # The width of the bars
# Plot the bars for each attribute
bar3 = ax2.bar(y - width, away_max_values, width, label='Away max values', color='green')
bar4 = ax2.bar(y, away_min_values, width, label='Away min values')
# Add value labels on top of each bar3
for p in bar3:
height = p.get_height()
ax2.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
ha='center', va='center', fontsize=15, color='black', fontweight='bold')
# Add value labels on top of each bar4
for p in bar4:
height = p.get_height()
ax2.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+3),
ha='center', va='center', fontsize=15, color='black', fontweight='bold')
# Add labels, title, and custom x-axis tick labels
#ax2.set_ylabel('Counts',fontsize=15,fontweight='bold')
ax2.set_title('Performance Comparison: Away Games', fontsize=20, fontweight='bold', color='black')
ax2.set_xticks(y-width/2)
ax2.tick_params(axis='x', labelsize=15, labelcolor='black') # Adjust the font size as needed
ax2.tick_params(axis='y', labelsize=15, labelcolor='black')
ax2.set_xticklabels(stats)
ax2.legend(loc='upper center', ncols=3,fontsize=15, labelcolor='black')
ax2.set_ylim(0, 70)
plt.tight_layout(pad=1);
subplots()
#'#800000'
This shows and track player shooting efficiencies accros 5 games
def fg_3pts_ft(player):
# Defining the fg_percent function to return data
def fg_percent(fg_made, fg_attempted):
if fg_attempted == 0:
return {'sizes': [0, 100], 'labels': ['made', 'missed'], 'colors': ['#15B01A', 'red']}
made_percentage = (fg_made / fg_attempted) * 100
missed_percentage = 100 - made_percentage
labels = ['made', 'missed']
sizes = [made_percentage, missed_percentage]
colors = ['#15B01A', 'red']
return {'sizes': sizes, 'labels': labels, 'colors': colors}
# Create a figure
fig = plt.figure(figsize=(20, 18),facecolor='orange')
style.use('ggplot')
# Set the spacing between subplots
plt.subplots_adjust(hspace=20)
# Define the grid layout
gs = gridspec.GridSpec(6, 5, width_ratios=[1, 1, 1, 1, 1])
# Create subplots
ax1 = fig.add_subplot(gs[0,:])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[1, 1])
ax4 = fig.add_subplot(gs[1, 2])
ax5 = fig.add_subplot(gs[1, 3])
ax6 = fig.add_subplot(gs[1, 4])
ax7 = fig.add_subplot(gs[2,:]) # Adjust the width for ax1
ax8 = fig.add_subplot(gs[3, 0])
ax9 = fig.add_subplot(gs[3, 1])
ax10 = fig.add_subplot(gs[3, 2])
ax11 = fig.add_subplot(gs[3, 3])
ax12 = fig.add_subplot(gs[3, 4])
ax13 = fig.add_subplot(gs[4,:]) # Adjust the width for ax1
ax14 = fig.add_subplot(gs[5, 0])
ax15 = fig.add_subplot(gs[5, 1])
ax16 = fig.add_subplot(gs[5, 2])
ax17 = fig.add_subplot(gs[5, 3])
ax18 = fig.add_subplot(gs[5, 4])
#######################################################################################
########################## FIELD GOALS #####################################
#######################################################################################
last_5_games = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail().reset_index(drop=True)
last_5_games["vs_opp_win_loss"] = last_5_games["Hm/Aw"]+' '+last_5_games["Opp"]+' '+last_5_games['W/L']
last_5_games["GM_DAY"] = last_5_games["Date"]+' '+last_5_games["vs_opp_win_loss"]
# Melting the DataFrame to create a clean format
last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['FGA', 'FGM'])
colors= ['red', 'green']
sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax1)
# Add value labels on top of each bar
for p in ax1.patches:
height = p.get_height()
ax1.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+2),
ha='center', va='center', fontsize=20, color='black', fontweight='bold')
# Customize the plot
ax1.set_ylim(0, 50) # Use set_ylim instead of ylim
ax1.set_title(f'{player} Last 5 Games Field Goals (Attempts and Made)', fontweight='bold', color='black',fontsize=25,)
ax1.set_xlabel('',fontsize=15, fontweight='bold', color='black')
# Set the x-axis tick label color to purple
ax1.tick_params(axis='x', colors='black', labelsize=15)
#######################################################################################
# Plot the pie chart for ax2
data1 = fg_percent(last_5_games.iloc[0, 7], last_5_games.iloc[0, 8])
ax2.pie(data1['sizes'], labels=data1['labels'],
colors=data1['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=126)
ax2.set_title('',fontweight='bold',fontsize=17,color='black')
ax2.axis('equal')
########################################################################################
# Plot the pie chart for ax3
data2 = fg_percent(last_5_games.iloc[1, 7], last_5_games.iloc[1, 8])
ax3.pie(data2['sizes'], labels=data2['labels'],
colors=data2['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=270)
ax3.set_title('',fontweight='bold',fontsize=17,color='black')
ax3.axis('equal')
#######################################################################################
# Plot the pie chart for ax4
data3 = fg_percent(last_5_games.iloc[2, 7], last_5_games.iloc[2, 8])
ax4.pie(data3['sizes'], labels=data3['labels'],
colors=data3['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=40,
textprops={'size': 'medium', 'color': 'black'})
ax4.set_title('Field Goals %',fontweight='bold',fontsize=17,color='black')
ax4.axis('equal')
#######################################################################################
# Plot the pie chart for ax5
data4 = fg_percent(last_5_games.iloc[3, 7], last_5_games.iloc[3, 8])
ax5.pie(data4['sizes'], labels=data4['labels'],
colors=data4['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=10,
textprops={'size': 'medium', 'color': 'black'})
ax5.set_title('',fontweight='bold',fontsize=17,color='black')
ax5.axis('equal')
#######################################################################################
# Plot the pie chart for ax6
data5 = fg_percent(last_5_games["FGM"][4], last_5_games["FGA"][4])
ax6.pie(data5['sizes'], labels=data5['labels'],
colors=data5['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},
startangle=60)
ax6.set_title('',fontweight='bold',fontsize=17,color='black')
ax6.axis('equal')
#######################################################################################
########################## 3 POINTS #####################################
#######################################################################################
# Melting the DataFrame to create a clean format
last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['3PA', '3PM'])
colors= ['orange', 'green']
sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax7)
# Add value labels on top of each bar
for p in ax7.patches:
height = p.get_height()
ax7.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+1),
ha='center', va='center', fontsize=20, color='black', fontweight='bold')
# Customize the plot
ax7.set_ylim(0, 20) # Use set_ylim instead of ylim
ax7.set_title(f'3 Points (Attempts and Made)', fontweight='bold', color='black',fontsize=25,)
ax7.set_xlabel('',fontsize=15, fontweight='bold', color='black',)
# Set the x-axis tick label color to purple
ax7.tick_params(axis='x', colors='black',labelsize=15)
#######################################################################################
# Plot the pie chart for ax8
data6 = fg_percent(last_5_games.at[0, '3PM'], last_5_games.at[0, '3PA'])
ax8.pie(data6['sizes'], labels=data6['labels'],
colors=data6['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=126)
ax8.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax8.axis('equal')
########################################################################################
# Plot the pie chart for ax3
data7 = fg_percent(last_5_games.at[1, '3PM'], last_5_games.at[1, '3PA'])
ax9.pie(data7['sizes'], labels=data7['labels'],
colors=data7['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=270)
ax9.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax9.axis('equal')
#######################################################################################
# Plot the pie chart for ax4
data8 = fg_percent(last_5_games.at[2, '3PM'], last_5_games.at[2, '3PA'])
ax10.pie(data8['sizes'], labels=data8['labels'],
colors=data8['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=40,
textprops={'size': 'medium', 'color': 'black'})
ax10.set_title('3 Points %',fontweight='bold',fontsize=17,color='black',pad=5)
ax10.axis('equal')
#######################################################################################
# Plot the pie chart for ax5
data9 = fg_percent(last_5_games.at[3, '3PM'], last_5_games.at[3, '3PA'])
ax11.pie(data9['sizes'], labels=data9['labels'],
colors=data4['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=10,
textprops={'size': 'medium', 'color': 'black'})
ax11.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax11.axis('equal')
#######################################################################################
# Plot the pie chart for ax6
data10 = fg_percent(last_5_games.at[4, '3PM'], last_5_games.at[4, '3PA'])
ax12.pie(data10['sizes'], labels=data10['labels'],
colors=data5['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},
startangle=60)
ax12.set_title('',fontsize=17,fontweight='bold',color='black',pad=5)
ax12.axis('equal')
#######################################################################################
########################## FREE THROW #####################################
#######################################################################################
# Melting the DataFrame to create a clean format
last_5_games_melted = pd.melt(last_5_games , id_vars='GM_DAY', value_vars=['FTA', 'FTM'])
colors= ['red', '#00FF00']
sns.barplot(data=last_5_games_melted, x='GM_DAY', y='value', hue="variable", palette=colors, ax=ax13)
# Add value labels on top of each bar
for p in ax13.patches:
height = p.get_height()
ax13.annotate(f"{height:.0f}", (p.get_x() + p.get_width() / 2., height+1),
ha='center', va='center', fontsize=20, color='black', fontweight='bold')
# Customize the plot
ax13.set_ylim(0, 20) # Use set_ylim instead of ylim
ax13.set_title('Freethrows(Attempts and Made)', fontweight='bold', color='black',fontsize=25,)
ax13.set_xlabel('',fontsize=15, fontweight='bold', color='black',)
# Set the x-axis tick label color to purple
ax13.tick_params(axis='x', colors='black',labelsize=15)
#######################################################################################
# Plot the pie chart for ax8
data11 = fg_percent(last_5_games.at[0, 'FTM'], last_5_games.at[0, 'FTA'])
ax14.pie(data11['sizes'], labels=data11['labels'],
colors=data11['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=126)
ax14.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax14.axis('equal')
########################################################################################
# Plot the pie chart for ax3
data12 = fg_percent(last_5_games.at[1, 'FTM'], last_5_games.at[1, 'FTA'])
ax15.pie(data12['sizes'], labels=data12['labels'],
colors=data12['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},startangle=270)
ax15.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax15.axis('equal')
#######################################################################################
# Plot the pie chart for ax4
data13 = fg_percent(last_5_games.at[2, 'FTM'], last_5_games.at[2, 'FTA'])
ax16.pie(data13['sizes'], labels=data13['labels'],
colors=data13['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=40,
textprops={'size': 'medium', 'color': 'black'})
ax16.set_title('Freethrows %',fontweight='bold',fontsize=17,color='black',pad=5)
ax16.axis('equal')
#######################################################################################
# Plot the pie chart for ax5
data14 = fg_percent(last_5_games.at[3, 'FTM'], last_5_games.at[3, 'FTA'])
ax17.pie(data14['sizes'], labels=data14['labels'],
colors=data14['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%', startangle=10,
textprops={'size': 'medium', 'color': 'black'})
ax17.set_title('',fontweight='bold',fontsize=17,color='black',pad=5)
ax17.axis('equal')
#######################################################################################
# Plot the pie chart for ax6
data15 = fg_percent(last_5_games.at[4, 'FTM'], last_5_games.at[4, 'FTA'])
ax18.pie(data15['sizes'], labels=data15['labels'],
colors=data15['colors'],
wedgeprops=dict(width=0.3),
autopct='%1.1f%%',
textprops={'size': 'medium', 'color': 'black'},
startangle=60)
ax18.set_title('',fontsize=17,fontweight='bold',color='black',pad=5)
ax18.axis('equal')
######################################################################################
# Adjust layout
#plt.tight_layout()
fig.tight_layout(pad=1)
# Show the plot
plt.show()
#xx-small, x-small, small, medium, large, x-large, xx-large, larger, smaller
def last_10_games(player, nugget_regular_season_df):
# Create a figure
fig = plt.figure(figsize=(30, 15), facecolor='green')
style.use('dark_background')
# Set the spacing between subplots
plt.subplots_adjust(hspace=0.3, wspace=0.2) # Adjust the value as needed
# Define the grid layout you want to use
gs = gridspec.GridSpec(2, 5, width_ratios=[1, 1, 1, 1, 1])
# Set the overall title for the figure
fig.suptitle(f"{player}'s Scoring Pattern Distribution Over The Last 10 Games", color='orange', fontsize=30, fontweight='bold')
# Prepare the data
last_10_games = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail(10).reset_index(drop=True)
last_10_games["vs_opp_win_loss"] = last_10_games["Hm/Aw"]+' '+last_10_games["Opp"]+' '+last_10_games['W/L']
last_10_games["GM_DAY"] = last_10_games["Date"]+' '+last_10_games["vs_opp_win_loss"]
pie_df = last_10_games[["GM_DAY",'PLAYER', '3PM', '2PM', 'FTM','Pts' ]]
#pie_df = pie_concat_df[['Country', '3PM', '2PM', 'FTM']].reset_index(drop=True)
pie_df.loc[:, "3PM"] = pie_df["3PM"] * 3
pie_df.loc[:, "2PM"] = pie_df["2PM"] * 2
last_ten_games = pie_df
# Create subplots
axes = [
fig.add_subplot(gs[0, 0], facecolor='black'),
fig.add_subplot(gs[0, 1], facecolor='black'),
fig.add_subplot(gs[0, 2], facecolor='black'),
fig.add_subplot(gs[0, 3], facecolor='black'),
fig.add_subplot(gs[0, 4], facecolor='black'),
fig.add_subplot(gs[1, 0], facecolor='black'),
fig.add_subplot(gs[1, 1], facecolor='black'),
fig.add_subplot(gs[1, 2], facecolor='black'),
fig.add_subplot(gs[1, 3], facecolor='black'),
fig.add_subplot(gs[1, 4], facecolor='black')
]
# Define colors
cmaps = ["Reds", "Reds", "Blues", "Reds", "Reds", "Blues", "Blues", "Reds", "Blues", "Blues"]
labels = ['3PM', '2PM', 'FTM']
# Plot the pie charts
for i, ax in enumerate(axes):
if i < len(last_ten_games):
piechart_df = last_ten_games.loc[i, labels]
cmap = plt.get_cmap(cmaps[i % len(cmaps)])
colors = cmap([0.1, 0.5, 0.9])
ax.pie(piechart_df.values, autopct="%1.1f%%", labels=piechart_df.index, colors=colors, startangle=90, shadow=True, textprops={'size': 'xx-large', 'color': 'black'})
ax.set_title(last_ten_games.loc[i, "GM_DAY"], fontweight='bold', fontsize=25, color='black')
ax.axis('equal')
plt.show()
def stats_performance_trend(player, stat, label, color):
style.use('dark_background')
plt.figure(figsize=(30, 7))
line_df = nugget_regular_season_df[nugget_regular_season_df.PLAYER == player ].tail(20)
nugget_line_group_df = line_df.groupby(["GM_DAY"])[['MIN','Pts','FGA','FGM','3PM','3PA', 'FTM',
'FTA', 'OREB','REB', 'AST', 'STL',
'BLK', 'TOV', 'PF']].sum().astype(int)
# Plot the lines for each stats
plt.plot(nugget_line_group_df.index, nugget_line_group_df[stat], label=label, marker='o', color=color)
# Add data labels to each point
for x, y in zip(nugget_line_group_df.index, nugget_line_group_df[stat]):
plt.text(x, y, str(y), ha='center', va='bottom', fontsize=15, color='white')
#for x, y in zip(nugget_line_group_df.index, nugget_line_group_df['Opp_PTS']):
# plt.text(x, y, str(y), ha='center', va='bottom', fontsize=15, color='white')
# Customize the plot
plt.title(f" 2024-2025 NBA Regular Season {player} {label} last 20 games.", fontsize=25, fontweight='bold', color='white')
plt.xlabel('Date',color='orange',fontsize=25, fontweight='bold')
plt.xticks( rotation=30,fontsize=20, fontweight='bold',color='yellowgreen')
plt.yticks(fontsize=20, fontweight='bold',color='white')
legend = plt.legend(fontsize=30)
for text in legend.get_texts():
text.set_color("orange")
text.set_fontsize(20)
text.set_fontweight('bold')
# Show the plot
plt.grid()
plt.show()
len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Nikola Jokic"])].reset_index(drop=True))
statistical_summary("PLAYER", "Nikola Jokic", "Nikola Jokic", "Greens")
The chart above shows the statitical summary of the Nikola Jokic's 2024/2025 regular season. These colors represent different levels of performance.
Current Regular season performace:
Field Goals (FG):
AST (AST):
This statitical summary provides a comprehensive overview of Nikola Jokic's performance across various metrics during the regular season. 🏀🔥
performance_trend(nugget_regular_season_df, 'Nikola Jokic','Reds')
Washington DC (December 08, 2024):
Oklahoma City Thunders (October 24 and December 14, 2024): -With an opening game for the denver nuggets jokic recorded his first tripple double of the season playing 35 minutes, scored 16 points and 12 rebounds and 13 assists. Also, against th LA Clippers, he recorded 16 points, 7 rebounds and 2 assists.
Oklahoma City Thunders and Cleveland Cavaliers (November 7, 2024 and December 06, 2024):
Phoenix Suns (December 24, 2024):
Scoring Performance: Jokic's scoring ability is highlighted by his 56-points game against the Washington DC, indicating a strong offensive performance. However, this contrasts with two low-scoring games where he only scored 2 points against both the Houston Rockets and Boston Celtics, despite playing significant minutes (39 and 41 respectively). This suggests inconsistency in scoring.
Playing Time: Gordon's playing time does not seem to correlate directly with his scoring, as seen in the games against the Rockets and Celtics where he played a lot of minutes but scored 2 points each.
Rebounding Ability: His rebounding performance shows variability. He secured a season-high 15 rebounds against the Sacramento Kings. Yet, there was a game against the Indiana Pacers where he only managed 1 rebound in 32 minutes, which could point to an off night or strong opposition defense.
Overall Assessment: Gordon shows potential for high-scoring games and strong rebounding but also has instances of low productivity. This could be due to various factors such as the opposing team's defense, his physical condition during the games, or the strategies employed by his own team.
players_df = nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(['Nikola Jokic'])]
nugget_regular_season_df.groupby(['PLAYER'])['AST'].mean()
annotation_text = (
"This Joker is having another outstanding season in 2024-25!\n"
"Here are some of his key stats so far:\n"
"15,000 + career points in NBA\n"
"- Average Points per Game (PTS): 31.5 (1st in the league)\n"
"- Rebounds per Game (REB): 13.0 (3rd in the league)\n"
"- Assists per Game (AST): 9.7 (2nd in the league)\n"
"- Field Goal Percentage (FG%): 56.2\n"
"- Three-Point Percentage (3P%): 49.6\n"
"- Efficiency Per Game (EFF) 42.4 (1st in the league)\n"
"- Triple-Doubles: 14 in 31 games (1st in the league)\n"
"- Double-Doubles: 11 in 31 games\n"
"Jokic continues to be a dominant force,\n"
"and a turbo engine for the nuggets also leading the league\n"
"in player efficiency rating.\n"
"He's also in the MVP race.\n "
"It's impressive to see him maintain such high performance\n"
"levels consistently. "
"Do you think he'll win the MVP this season?"
)
season_average("Nikola Jokic", "nikola.jpg", annotation_text, "yellow", plt.get_cmap("Wistia"))
triple_double_df = tripple_double_double('Nikola Jokic')
triple_double_overview()
Total triple-double performances for Nikola Jokic: 14 Total double-double performances for Nikola Jokic: 11
The chart lists game dates and Jokic's triple-double stats for each game, showcasing his consistent high performance in points, rebounds, and assists throughout the season.
total_stats = players_total_stats("PLAYER", "Nikola Jokic")
overall_stats("Nikola Jokic", 1500)
Nikola Jokic's 2024/2025 regular season performance based on the aggregate sum of his statistical features:
These statistics provide a snapshot of the Nikola Jokic's performance across various aspects of the game. 🏀🏀🐼👨💻
home_away_performances("Nikola Jokic")
Home Court: Nikola Jokic seems to perform slightly better in terms of scoring when playing at away games. He scored more points at away compared to home and grabbed 20 total rebounds each both home and away. 16 assists each.
Offensive Effort: Nikola Jokic appears to performed some what equally in both home and away games in terms of total (Rebounds, Assists and Blocks).
Overall, Nikola Jokic's performance metrics indicate that his efficiency at home is higher than his away performance, but in scoring, he recorded more away points in total. However, his rebounding, assists and blocks in away games are both equal.
fg_3pts_ft("Nikola Jokic")
last_10_games('Nikola Jokic', nugget_regular_season_df)
The image displays Nikola Jokic's scoring pattern distribution over the last 10 games, represented by pie charts for each game. Each chart is divided into three sections: 3PM (three-point made), 2PM (two-point made), and FTM (free throws made). Here’s a detailed analysis of the data:
Consistency in Two-Point Shots: Jokic’s scoring is heavily reliant on two-point shots, consistently making up the majority of his points in each game. This indicates a strong inside game and mid-range shooting ability.
Three-Point Shooting: The percentage of points from three-pointers varies significantly, with the highest being 37.5% and the lowest being 0.0%. Improving consistency in three-point shooting could make Jokic’s scoring more versatile and unpredictable.
Free Throws: The contribution of free throws to Jokic’s scoring is relatively low, with the highest being 22.9% and the lowest being 3.7%. Increasing free throw attempts and accuracy could add valuable points, especially in close games.
Enhance Three-Point Shooting: Focus on improving three-point shooting consistency to add another dimension to Jokic’s scoring.
Increase Free Throw Attempts: Work on drawing more fouls and improving free throw accuracy to capitalize on easy scoring opportunities.
Maintain Strong Inside Game: Continue to leverage the strong two-point shooting, which is a significant part of Jokic’s scoring arsenal.
Balanced Scoring Approach: Aim for a balanced scoring approach, ensuring that Jokic can adapt to different defensive strategies and maintain scoring efficiency.
These insights and recommendations should help in understanding Jokic's scoring patterns and identify areas for potential improvement.
stats_performance_trend("Nikola Jokic", 'Pts', 'Points', 'green')
stats_performance_trend("Nikola Jokic", 'REB', 'Rebounds', 'green')
stats_performance_trend("Nikola Jokic", 'AST', 'Assists', 'green')
stats_performance_trend("Nikola Jokic", 'BLK', 'Blocks', 'green')
stats_performance_trend("Nikola Jokic", 'TOV', 'Turnovers', 'green')
Line Chart depicting the performances of metric like Points, Rebounds, Assists, Steals and Turnovers across the last 20 games played
High Scoring Peaks: Jokic has several high-scoring games, with peaks around 56 points on December 8, 2024, 49 points on December 9 and 46 January 5, 2025. These peaks indicate his ability to deliver standout performances.
Consistency: Despite some fluctuations, Jokic consistently scores in the mid-20s to low-30s range, showing his reliability as a scorer.
Variability: There are a few games where his points dip significantly, suggesting possible defensive strategies by opponents or off-nights.
Consistent High Performance: Nikola Jokic consistently achieves high points, rebounds, and assists across multiple games, indicating his reliability and versatility as a player.
Field Goal Efficiency: Jokic's field goal percentages are generally high, with notable performances in games against teams like Utah and Spurs in the last 5 games. This suggests strong shooting accuracy.
Rebounding Strength: Jokic frequently records double-digit rebounds, showcasing his dominance in securing the ball and contributing to his team's possession.
Assist Contribution: His assist numbers are also impressive, highlighting his playmaking abilities and his role in facilitating team offense.
performance_trend(nugget_regular_season_df, 'Aaron Gordon','viridis_r')
performance_trend(nugget_regular_season_df, 'Jamal Murray','viridis_r')
len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Michael Porter"])].reset_index(drop=True))
performance_trend(nugget_regular_season_df, 'Michael Porter','Reds')
total_stats = players_total_stats("PLAYER", "Michael Porter")
overall_stats("Michael Porter", 1000)
nugget_regular_season_df.groupby(['PLAYER'])['EFF'].mean()
annotation_text = (
"The One time NBA Champion Michael Porter in this season with the nuggets so far.\n"
"Here are some of his average stats so far:\n"
"- Average Points per Game (PTS): 19.1 \n"
"- Rebounds per Game (REB): 6.5 \n"
"- Assists per Game (AST): 2.6\n"
"- Field Goal Percentage (FG%): 52.4\n"
"- Three-Point Percentage (3P%): 43.3\n"
"- Free Throw Percentage (FT%): 63.0\n"
"- Efficiency Per Game (EFF) 20.6\n"
"- Double-Doubles: 5\n"
"Micheal Porter continues to be a contributing force,\n"
)
season_average("Michael Porter", "denver.jpg", annotation_text, "yellow", plt.get_cmap("winter"))
home_away_performances("Michael Porter")
tripple_double_double("Michael Porter")
fg_3pts_ft("Michael Porter")
last_10_games('Michael Porter', nugget_regular_season_df)
stats_performance_trend("Michael Porter", 'Pts', 'Points', 'green')
stats_performance_trend("Michael Porter", 'REB', 'Rebounds', 'green')
stats_performance_trend("Michael Porter", 'AST', 'Assists', 'green')
stats_performance_trend("Michael Porter", 'BLK', 'Blocks', 'green')
stats_performance_trend("Michael Porter", 'TOV', 'Turnovers', 'green')
performance_trend(nugget_regular_season_df, 'Christian Braun','viridis_r')
len(nugget_regular_season_df[nugget_regular_season_df['PLAYER'].isin(["Russell Westbrook"])].reset_index(drop=True))
performance_trend(nugget_regular_season_df, 'Russell Westbrook','Reds')
total_stats = players_total_stats("PLAYER", "Russell Westbrook")
overall_stats("Russell Westbrook", 700)
annotation_text = (
"This triple double king is having a good season with the nuggets so far.\n"
"Starting off the the bench in most games,\n"
"His playing styles correltes with the nugget's\n"
"Here are some of his avearge stats so far:\n"
"- Average Points per Game (PTS): 12.0 \n"
"- Rebounds per Game (REB): 4.6 \n"
"- Assists per Game (AST): 6.6\n"
"- Field Goal Percentage (FG%): 45.4\n"
"- Three-Point Percentage (3P%): 31.6\n"
"- Free Throw Percentage (FT%): 63.0\n"
"- Efficiency Per Game (EFF) 16.4\n"
"- Triple-Doubles: 2\n"
"- Double-Doubles: 4\n"
"Russell Westbrook continues to be a contributing force,\n"
"especially on defensive end for the nuggets.\n"
"It's impressive to see him maintain such high performance\n"
"levels consistently. "
)
season_average("Russell Westbrook", "denver.jpg", annotation_text, "yellow", plt.get_cmap("winter_r"))
nugget_regular_season_df.groupby(['PLAYER'])['EFF'].mean()
home_away_performances("Russell Westbrook")
tripple_double_double("Russell Westbrook")
fg_3pts_ft("Russell Westbrook")
last_10_games('Russell Westbrook', nugget_regular_season_df)
stats_performance_trend("Russell Westbrook", 'Pts', 'Points', 'green')
stats_performance_trend("Russell Westbrook", 'REB', 'Rebounds', 'green')
stats_performance_trend("Russell Westbrook", 'AST', 'Assists', 'green')
stats_performance_trend("Russell Westbrook", 'BLK', 'Blocks', 'green')
stats_performance_trend("Russell Westbrook", 'TOV', 'Turnovers', 'green')
stats_performance_trend("Peyton Watson", 'MIN', 'Minutes', 'green')
stats_performance_trend("Peyton Watson", 'Pts', 'Points', 'green')
stats_performance_trend("Peyton Watson", 'REB', 'Rebounds', 'green')
stats_performance_trend("Peyton Watson", 'AST', 'Assists', 'green')
stats_performance_trend("Peyton Watson", 'BLK', 'Blocks', 'green')
stats_performance_trend("Peyton Watson", 'TOV', 'Turnovers', 'green')
players_df = nugget_regular_season_df[nugget_regular_season_df['Date'].isin(['2025-01-09'])]
players_df = players_df.groupby(["PLAYER"])[['MIN','Pts','REB','AST', 'BLK',
'TOV', 'FGA', 'FGM',
'3PA', '3PM','2PM','2PA',"OREB","DREB",
'FTA', 'FTM', 'PF','+/-','EFF']].sum().astype(int).sort_values(by='Pts',ascending=False)#.head(10)
players_df
| MIN | Pts | REB | AST | BLK | TOV | FGA | FGM | 3PA | 3PM | 2PM | 2PA | OREB | DREB | FTA | FTM | PF | +/- | EFF | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| PLAYER | |||||||||||||||||||
| Jamal Murray | 34 | 21 | 3 | 9 | 1 | 2 | 13 | 7 | 6 | 4 | 3 | 7 | 0 | 3 | 3 | 3 | 1 | 24 | 27 |
| Michael Porter | 27 | 19 | 8 | 2 | 0 | 2 | 12 | 8 | 5 | 3 | 5 | 7 | 0 | 8 | 0 | 0 | 1 | 15 | 24 |
| Russell Westbrook | 29 | 19 | 6 | 8 | 0 | 3 | 16 | 8 | 5 | 1 | 7 | 11 | 2 | 4 | 5 | 2 | 3 | 18 | 19 |
| Julian Strawther | 32 | 16 | 4 | 2 | 0 | 1 | 10 | 5 | 8 | 4 | 1 | 2 | 0 | 4 | 2 | 2 | 3 | 18 | 16 |
| Christian Braun | 28 | 15 | 2 | 1 | 0 | 0 | 8 | 6 | 2 | 2 | 4 | 6 | 0 | 2 | 1 | 1 | 4 | 11 | 17 |
| DeAndre Jordan | 23 | 12 | 9 | 2 | 0 | 0 | 6 | 5 | 0 | 0 | 5 | 6 | 2 | 7 | 3 | 2 | 2 | 17 | 23 |
| Peyton Watson | 24 | 9 | 4 | 1 | 2 | 1 | 10 | 4 | 2 | 0 | 4 | 8 | 0 | 4 | 3 | 1 | 3 | 17 | 8 |
| Dario Saric | 20 | 7 | 7 | 2 | 0 | 2 | 8 | 3 | 4 | 1 | 2 | 4 | 3 | 4 | 0 | 0 | 2 | 11 | 10 |
| Jalen Pickett | 4 | 6 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | -5 | 6 |
| Zeke Nnaji | 4 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 2 | -5 | 2 |
| Hunter Tyson | 9 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | -1 | 0 |
| Trey Alexander | 4 | 0 | 1 | 2 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | -5 | 1 |
style.use("dark_background")
# Calculate the percentage of each country
pct_df = players_df['Pts']
pct_df_pct = ((pct_df / pct_df.sum()) * 100).round(decimals=1)
# Create the bar plot
plt.figure(figsize=(5, 5))
bar_plot = sns.barplot(x=pct_df_pct, y=pct_df_pct.index, palette='viridis')
plt.ylabel(None)
plt.xlabel('Percentage')
# Add values on the bars
for index, value in enumerate(pct_df_pct):
plt.text(value + 5, index, f'{value}%', va='center', ha='center', fontsize=12, fontweight='bold', color='orange')
plt.title('Percentage Distribution of Points vs Atlanta Hawks 2025-01-02', fontsize=15, fontweight='bold', color='white', pad=20)
plt.xticks(fontsize=14, color='yellowgreen')
plt.yticks(fontsize=14, color='yellowgreen')
plt.grid(False)
# Remove the rectangular borderlines
for spine in bar_plot.spines.values():
spine.set_visible(False)
plt.tight_layout()
plt.show()